* Program subject to change
Level: Intermediate
Track: Health Policy & Regulatory
The value of medical innovation depends on the perspective. Registration authorities (EMA, FDA) mainly consider the clinical value of the medical innovation, whereas national health authorities take a broader perspective by including clinical, economic criteria, and potential other criteria like equity and social values. Value-based pricing is the most widely accepted approach in the pricing and reimbursement process in Europe, which varies from the narrow concept based on the incremental cost-effectiveness ratio (ICER) threshold to broader approaches. Value-based pricing determines the maximum price from the national payer perspective. This price should exceed the minimum price for the investor acting in the international financial market, which is based on economic valuation theory. Finally, there are other stakeholders, eg, patients, physicians’ healthcare insurers, employers, with their specific assessment of the value of medical innovation varying from, respectively, quality of life, effectiveness, budget impact, and costs of lost productivity. This course offers an overview of the perspectives of the relevant stakeholders and their respective data requirements for value assessment of innovative drugs. The course will then describe in-depth description of the various value-based pricing methods, eg, ICER, multicriteria decision analysis (MCDA), comparative effectiveness research (CER), and relative effectiveness (RE). We include examples of orphan drugs and ATMPs which are most striking to illustrate the concepts, but we also include value assessment for more traditional innovative drugs in broad indications. Familiarity with health economic evaluation is desirable, but the course assumes little or no familiarity with economic valuation theory.
Afschin Gandjour, MD, PhD, MA, MBA
Frankfurt School of Finance & Management, Frankfurt, Germany
Lou Garrison, PhD
University of Washington, Seattle, WA, USA
Marlene Gyldmark, MPhil
Idorsia Pharmaceuticals, Allschwill, Switzerland
Mark Nuijten, MBA, PhD, MD
Ben Gurion University, Be'er Sheva, Israel
Fred W. Sorenson, MSc
Cencora, Basel, Switzerland
Level: Introductory
Track: Methodological & Statistical Research
The rapid advancement in generative artificial intelligence (Gen AI) presents an opportunity for transformative potential in the field of Health Economics and Outcomes Research (HEOR). This course provides an introductory understanding of generative AI models with a particular focus on large language models (LLMs), which are revolutionizing the field of HEOR. Participants will be provided with an overview of the most appropriate ways to access LLMs, going beyond the use of chatbots. Further, they will be given insights into how to use prompt engineering to conduct scientific research and gain an understanding on issues pertaining to privacy and security when using Gen AI for HEOR. Participants will further explore specific applications of these models for conducting robust scientific HEOR research in systematic literature review (SLR), real-world evidence analysis, and economic evaluation. The course aims to equip participants with the knowledge to begin to use generative AI techniques for specific HEOR contexts and to appreciate how these innovative approaches can enhance HEOR activities. Practical exercises using Python and relevant AI frameworks will be incorporated for participants to follow along. Participants who wish to gain hands-on experience are required to bring their laptops with Python installed.
PREREQUISITES: Students should have a general understanding of common HEOR concepts such as SLRs and cost-effectiveness models. Knowledge of Python or similar programming languages such as R is considered a benefit, but not required.
Sven Klijn, MSc
Bristol Myers Squibb, Utrecht, ZH, Netherlands
William Rawlinson, MPhysPhil
Estima Scientific Ltd, London, United Kingdom
Tim Reason, MSc
Estima Scientific Ltd, South Ruislip, LON, United Kingdom
Track: Health Technology Assessment
As the cost of bringing a new health technology to market continues to climb, more and more firms, developers, and investors are searching for tools to prioritize their efforts on the technologies with the greatest potential for clinical impact and market viability. While health economic analysis has long been established as a necessity to inform decision making for market access and reimbursement, it is increasingly being used at earlier stages of product development for healthcare and life sciences to increase the access rate of R&D and efficiently prioritize data collection. The number of available methods for this field has continued to expand.
This course aims to demystify the objectives of early-stage health technology assessment and the methods of translational health economics. Students in the course will gain a thorough understanding of available methods for early-stage technology assessment, the specific challenges and solutions, and a clear sense of how to implement this in the complexity of health technology development, funding, regulation, pricing, and reimbursement. The course will utilize real-world examples and students will have the opportunity to strategize about the creation of a research plan for their purposes.
PREREQUISITE: Familiarity with key elements, methods, language, and basic health technology principles are prerequisites to attending this course.
William Canestaro, PhD, MSc
Washington Research Foundation, Seattle, WA, USA
Erik Landaas, PhD, MPH
W. L. Gore & Associates, Inc., Flagstaff, AZ, USA
Lotte Steuten, MSc, PhD
Office of Health Economics, London, LON, United Kingdom
Level: Experienced
Track: Real World Data & Information Systems
In recent years, real-world evidence (RWE) has been increasingly used to inform regulatory, payer, and health technology assessment (HTA) decisions, as well as clinical guideline development. In addition, it has been recognized that the analysis of hypothetical estimands in clinical trials is necessary when the standard intention-to-treat (ITT) analysis does not answer the decision problem, usually because of treatment switching. An innovative framework for causal inference methods, target trial emulation, causal estimands and causal modeling guides the design and analysis of observational studies and clinical trials. This course will (1) introduce causal principles, causal diagrams (directed acyclic graphs; DAGs), and target trial emulation to avoid self-inflicted biases (e.g., time-zero bias, immortal time bias), (2) provide an overview of causal methods for baseline confounding (multivariate regression, propensity scores) and time-varying confounding (e.g., g-formula, marginal structural models with inverse probability of treatment weighting, and rank-preserving structural failure-time models with g-estimation), (3) propose appropriate estimands to ensure decision problems are directly addressed when analyzing observational data or data from clinical trials affected by treatment switching, (4) present lessons learned from applied case examples in HTA, such as single arm-trials with external control arms or trials affected by treatment switching, (5) provide recommendations regarding the use of causal inference methods and estimands and their application in causal modeling, and (6) discuss acceptance and barriers from an HTA agency perspective. The target audience includes all stakeholders and researchers from all fields in health and healthcare.
PREREQUISITE: Students are expected to have a basic knowledge in epidemiologic studies and methods (including the concept of confounding).
Felicitas Kuehne, MSc
UMIT - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Innsbruck, Austria
Nicholas Latimer, PhD, MSc
University of Sheffield & Delta Hat Limited, Sheffield, DBY, Great Britain
Uwe Siebert, MD, MPH, MSc, ScD
UMIT TIROL - University for Health Sciences and Technology Hall in Tirol, Austria and Harvard Chan School of Public Health Harvard University, Hall in Tirol, 7, Austria
Gain new skills in the use of real-world evidence (RWE) in external control arms (ECAs) – an advanced application that has become critical in drug development. During this course you will learn more about the history and regulatory landscape of the application of ECAs and how to analyze the current regulatory and payer landscape for ECA acceptance and application in drug development and market access. You will explore the relevant data sources and sourcing methodologies for ECAs and learn how to develop external control arms using synthetic data and data from multiple data sources. End the course by designing and developing an external control arm with your peers.
PREREQUISITE: To get the most out of the course, students should be familiar with basic definitions and concepts of RWE including regulatory applications.
Ulka Campbell, PhD, MPH
Aetion, Inc, New York, NY, USA
Doug Foster, MBA
Advanced Data Sciences, San Francisco, CA, USA
Aaron Kamauu, MPH, MS, MD
Navidence LLC, Bountiful, UT, USA
Leanne Larson, MHA
ZS Associates, Wilmette, IL, USA
Track: Economic Evaluation
This highly practical course will outline the computational and transparency advantages of using R, for those used in health economic modelling using Microsoft Excel. This course explores the use of R for health economic modelling in the context of health economics and outcomes research (HEOR) and faculty will guide the participants through practical examples of HEOR. The faculty are expert speakers who have diverse experience in academia, national Health Technology Assessment agencies (NICE, NCPE), and industry. The faculty will lead participants through practical examples of health economic modelling including using R for Markov models from deterministic analysis through to probabilistic sensitivity analysis and EVPI. Additional useful packages for modelling using R will also be discussed. All sessions will interchange between descriptive lectures and hands-on exercises. Participants will be provided with materials, including model examples in R and information on where to go for further learning. This course is designed for those with some familiarity with modelling techniques, such as the concepts of discrete time cohort Markov models and probabilistic sensitivity analysis, but familiarity with R coding is not required. Attendees will require a laptop with RStudio (v1.1.0 or higher) and R (v4.2.1 or higher) downloaded and installed.
Gianluca Baio, PhD
University College London, London, United Kingdom
Rose Hart, PhD
Lumanity Inc., Sheffield, United Kingdom
Felicity Lamrock, PhD
Queens University Belfast, Belfast, ANT, United Kingdom
Howard Thom, MSc, PhD
University of Bristol, Bristol, United Kingdom
In this course, participants will be introduced to the principles of what makes real-world evidence (RWE) decision-grade, including an extended example. In the first half of the course, will review the most recent RWE frameworks and guidelines, and examine case studies in which RWE was used in regulatory and HTA approval. The second half of the course is an extended example in which participants will examine a study that could support an indication expansion, and interactively discuss how choices made in the design and implementation may affect the meaning and interpretability of results.PREREQUISITE: Students are expected to be familiar with relevant concepts and methodologies for analyzing real-world data.
Jeremy Rassen, ScD
Aetion, Inc., New York, NY, USA
Sebastian Schneeweiss, MD, ScD
Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Shirley Wang, PhD
Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
This course is designed to teach clinicians and new researchers how to incorporate health economics into study design and data analysis. Participants will first review the basic principles and concepts of health economic evaluations, then discuss how to collect and calculate the costs of different alternatives, determine the economic impact of clinical outcomes, and how to identify, track, and assign costs to different types of healthcare resources used. Different health economic models and techniques will be demonstrated including cost-minimization, cost-effectiveness, cost-benefit, cost-utility, and budget impact analysis. Decision analysis, sensitivity analysis, and discounting will all be demonstrated and practiced. This course is suitable for those with little or no experience with health economics.
Lorne Basskin, PharmD
Strategic Economics Ltd., Cary, NC, USA
Karen L Rascati, PhD, RPh
The University of Texas at Austin, Austin, TX, USA
Unlike marketing authorization for pharmaceuticals, mainly regulated at the European level by EMA, pricing and reimbursement decisions in Europe are managed by individual member states. Health care services are generally covered by a single public health insurer operating under the Ministry of Health supervision. As a monopoly buyer, this situation provides a leading position for the public health insurer to set reimbursement conditions. Therefore, based on each country’s set of regulations, processes, and values, wide variations exist in pricing and reimbursement decisions of pharmaceuticals. Using up-to-date governmental regulation sources and the ISPOR Global Health Care Systems Roadmap, this course will discuss health technology decision-making processes for reimbursement decisions for pharmaceuticals in France, Germany, Hungary, Italy, Poland, Spain, Sweden, and the UK. The course will describe these reimbursement systems, as well as compare, and bring into contrast their key characteristics. This course is designed for individuals with intermediate experience within a single healthcare system wishing to broaden their appreciation of other reimbursement systems.
Mondher Toumi, MD, PhD, MSc
Laboratoire de Santé Publique Aix-Marseille University, Marseille, France
Historically, economic models for cost-effectiveness analyses have been developed with specialized commercial software (such as TreeAge) or more commonly with spreadsheet software (almost always Microsoft Excel). But more recently there has been increasing interest in using R and other programming languages for cost-effectiveness analysis which can offer advantages regarding the integration of input parameter estimation and model simulation, the evaluation of structural uncertainty, and the quantification of decision uncertainty, among others. Programming languages such as R also facilitate reproducibility of model-based cost-effectiveness analysis which is more relevant than ever given recent calls for increased transparency. While these tools are still relatively new, there is an increased interest in learning opportunities as evidenced by recent tutorials, workshops, and development of open-source software.
In this short course, participants will learn how to use R to develop a number of different types of economic models to perform cost-effectiveness analysis. Economic models will include time-homogeneous and time-inhomogeneous Markov cohort models, partitioned survival models, and semi-Markov individual patient simulations. The underlying assumptions of each model type will be summarized and the implementation in R will be presented in an accessible manner. Participants will be asked to modify the models in R (eg, adding health states, use of alternative time-to-event distributions) and run analyses (eg, cost-effectiveness analysis, probabilistic sensitivity analysis, evaluating structural uncertainty, and value of information analysis). To make this interactive aspect of the course as efficient as possible, all participants will have access to the GitHub repository prior to the course. It will contain R code to run the economic models and R Markdown files to explain and reproduce the analyses covered in the course.
Devin Incerti, PhD
EntityRisk, Inc., San Francisco, CA, USA
Jeroen Jansen, PhD
Department of Clinical Pharmacy, School of Pharmacy, University of California – San Francisco, USA; PrecisionHEOR, Oakland, CA, USA
Track: Study Approaches
This course delves into the transformative role of Generative AI, particularly large language models (LLMs), in enhancing key areas of HEOR such as systematic literature reviews (SLR) and real-world evidence synthesis, economic modeling, regulatory decision-making, and the development of external control arms.
Participants will learn to apply Generative AI technologies to conduct comprehensive and efficient systematic literature reviews, synthesize evidence at scale, and construct robust economic models that support healthcare policy and market strategies. The course also covers the strategic use of AI in developing external control arms, essential for clinical trials and regulatory submissions, thereby improving the quality and speed of healthcare decisions.
This program is ideal for HEOR data analysts, outcome researchers, epidemiologist, health economists, regulatory affairs professionals, and anyone in all substance matter fields involved in the planning and implementation of healthcare strategies. Through a combination of expert lectures, hands-on exercises, and case study analyses, attendees will gain practical skills and insights into leveraging AI to streamline research processes, enhance data analysis, and forge ahead in the dynamic field of healthcare research and policy. Participants are required to bring a laptop with the capability to connect to Wi-Fi and sufficient processing power to handle basic analytical tasks. Access to an LMS for course materials, discussion boards, and any required pre-course or post-course work. For conducting SLR, participants may need access to databases such as PubMed, Cochrane Library, or Embase.
PREREQUISITE: Students are expected to have a basic knowledge of HEOR concepts, health data analysis, research methods, and interest in artificial intelligence.
Turgay Ayer, PhD
Georgia Institute of Technology, Atlanta, GA, USA
Jagpreet Chhatwal, PhD
Harvard Medical School, Boston, MA, USA
Xiaoyan Wang, PhD
Tulane Univeristy, New Orleans, LA, USA
Hua Xu, PhD
Yale University School of Medicine, New Haven, CT, USA
This short course is designed for professionals with a basic understanding of health technology assessment (HTA) who seek to deepen their knowledge and skills in evaluating interventions for rare diseases. Participants will gain an appreciation of the unique challenges that rare diseases pose for HTA due to limited evidence, small patient populations, and high treatment costs. A case study from Europe will be shared. The session will close with a discussion on the methodological challenges of HTA in rare disease.
Josh Byrnes, PhD, MHEcon, BEcon
Griffith University, Brisbane, QLD, Australia
Giuseppe Caputo, BSc (Hons)
Vista Health Pte Ltd., Singapore, Singapore
Hwee-Lin Wee, PhD
Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
Track: Patient-Centered Research
This course provides an in-depth discussion of the steps needed to successfully implement patient-reported outcomes (PRO) measurement within the drug development program to generate data to support patient-centered value messages. Formulation of a successful PRO strategy requires an understanding of PRO instrument selection, psychometric evaluation, data capture, and interpretation to negotiate regulatory, reimbursement, and market access drug development hurdles. Judging PRO instrument quality and appropriateness can be challenging.
The course will present the key elements to consider at each step in reviewing and selecting PRO measures and determining the need for new instruments. In addition, participants will gain a better understanding of regulatory expectations for qualitative and quantitative evidence to support the quality of PRO measures and aspects to consider when interpreting meaningful change. The course will include interactive discussions of PRO success stories and common pitfalls to watch out for during PRO implementation in clinical trial programs.
Participants will gain the knowledge and skills required to take on a more active and confident role in the PRO strategy and implementation process.
PREREQUISITE: This course assumes that participants will have a basic knowledge of key PRO-related concepts (eg, health-related quality of life, symptoms, impacts, a general knowledge of the PRO development steps, and a working knowledge of PRO measurement within clinical programs.)
Rebecca Crawford, MA
RTI Health Solutions, Manchester, United Kingdom
Lynda Doward, MRes
Ari Gnanasakthy, MSc, MBA
RTI Health Solutions, Succasunna, NJ, USA
Shanshan Qin, PhD
RTI Health Solutions, Durham, NC, USA
Nicholas J. Rockwood, PhD
RTI Health Solutions, Bend, OR, USA
Healthcare data are often available to payers and healthcare systems in real time, but are massive, high dimensional, and complex. Artificial intelligence and machine learning merge statistics, computer science, and information theory and offer powerful computational tools to enhance the extraction of useful information from complex healthcare data and prediction accuracy. This course gives an overview of basic machine learning concepts and introduces a few commonly used machine learning techniques and their practical applications in healthcare and pharmaceutical outcomes research. Participants will be introduced to foundational principles and concepts of statistical machine learning, then be provided with several specific machine learning techniques and their applications in health and pharmaceutical outcomes research. The course faculty will use R or Radiant to demonstrate several machine learning methods such as penalized regression and tree-based methods, as well as techniques for dimension reduction/feature selection. Participants will have hands-on practical experiences with machine learning and gain experience interpreting and evaluating the results and prediction performance that comes from machine learning modeling. Distinguishing prediction modeling from causal inference research in pharmacoepidemiology will be also presented and discussed. This is an entry-level course but is designed for those with some familiarity with traditional statistical modeling techniques (eg, linear regression, logistic regression).
PREREQUISITES: To get the most out of the course, students should have a basic statistical background. Participants who wish to gain hands-on experience are required to bring their laptops with Radiant (https://radiant-rstats.github.io/docs/install.html) installed.
Wei-Hsuan Jenny Lo-Ciganic, PhD, MSPharm, MS
University of Pittsburgh, Pittsburgh, PA, USA
William Padula, PhD, MSc, MS
University of Southern California, Los Angeles, CA, USA
Survival modeling techniques are commonly used to extrapolate clinical trial outcomes like overall survival to a time horizon that is appropriate for health economic evaluations. Standard parametric distributions, such as the exponential and Weibull, have been the de-facto standard for conducting such extrapolations but, with the advent of novel potentially curative therapies, these standard parametric distributions fail to capture the underlying survival trend. Newer techniques like parametric mixture, mixture-cure, and non-mixture cure models are among novel ways to capture these more complex survival patterns. In addition, the incorporation of external evidence has gained prominence. The purpose of this course is to enable participants to identify which methods are most appropriate in a specific context, considering underlying structural assumptions, and discuss how modeling choices propagate into health economic evaluations. To gain a more in-depth understanding of the impact of the choice for a specific method, there will be walkthroughs of exercises which participants will be able to practice in their own time.
PREREQUISITE: Participants must have a basic understanding of Kaplan-Meier curves, standard survival modeling techniques, such as a Weibull distribution in a partitioned survival framework, and R in order to follow along with the exercise walkthroughs.
Elisabeth Fenwick, PhD
OPEN Health Evidence & Access, Oxford, United Kingdom
Bristol-Myers Squibb, Utrecht, ZH, Netherlands
Claire Simons, PhD, MSc, MMATH
OPEN Health - HEOR and Market Access, York, United Kingdom
Health technology evaluation processes - such as health technology assessment (HTA) - are used to inform decision-makers and payers about the comparative value of treatments. The parameters used to make these decisions typically focus on patient and population outcomes and healthcare costs, such as cost-effectiveness or budget impact. Standard HTAs generally do not address healthcare system capacity (e.g., burdened workforce and facilities and their impacts on access and outcomes) and as such may over- or under-estimate the potential value of an intervention. In reality, capacity constraints can limit the implementation of health technologies in clinical practice and treatment delays may lead to worsened health outcomes for patients. This is becoming an increasing problem given the growing demands on healthcare systems from ageing populations and mounting chronic diseases. Accounting for capacity constraints in HTAs will better reflect the true value of novel interventions in real-world healthcare systems.
In the context of ophthalmology, there are several treatments available that reduce the burden on healthcare systems, such as through reducing the number of clinic visits and medical interventions. HTA economic models typically recognise such dimensions of value only as cost offsets; subsequent effects of releasing health system capacity and reducing the strain on overburdened health systems are not captured; neither are associated improvements in patient access and outcomes.
This panel will discuss how, through the ophthalmology lens, value assessment processes can optimise resource allocation and enhance the sustainability of healthcare systems, shaping system value.
F. Hoffmann-La Roche
Clifford Goodman, PhD
Lund University, Falls Church, VA, USA
Meindert Boysen, PharmD, MSc
National Institute for Health and Care Excellence (NICE), London, United Kingdom
Paula Head, BPharm
Roche, Welwyn Garden City, Hertfordshire, United Kingdom
Luke Nicholson, MD
Moorfields Eye Hospital, London, London, United Kingdom
Digital Conference Pass
Yannis Natsis, MA, BA
European Social Insurance Platform (ESIP), Brussels, Belgium
Richard Bergstrom, .
IQVIA, Zug, Switzerland
Pedro Facon, .
National Institute for Health and Disability Insurance, Brussels, Belgium
Birgitte Klindt Poulsen, .
Danish Medicines Council, Copenhagen, Denmark
Valérie Paris, MSc
OECD, Paris, Ile-de-France, France
ISSUE: Global drug pricing stands as a critical and hotly debated issue in the healthcare industry; its complexity underscores the need for in-depth discussion and debate amongst key stakeholders, especially considering the evolving EU Joint Clinical Assessment (JCA) process and presence of Institute of Clinical and Economic Review (ICER). This issue panel aims to ignite a debate on how manufacturers can build informed pricing strategies, grounded in economically justified prices supported by economic models, robust willingness-to-pay research, and extensive analysis of the analogue landscape.
OVERVIEW:
Priti Jhingran, PhD
Genesis Research Group, Hoboken, NJ, USA
Christopher Blanchette, PhD, MBA
Novo Nordisk, Plainsboro, NJ, USA
Frank-Ulrich Fricke, PhD
Technische Hochschule Nürnberg, Nürnberg, Germany
Adam Weston, MSc
Genesis Research Group, London, England, United Kingdom
ISSUE:
Chris Skedgel, PhD
Office of Health Economics, London, United Kingdom
Saskia Knies, PhD
Zorginstituut Nederland, Diemen, Netherlands
Tara Lavelle, PhD
Tufts University School of Medicine and Tufts Medical Center, Boston, MA, USA
Becky Pennington, MSc
University of Sheffield, Sheffield, United Kingdom
ISSUE: Individual preferences with respect to the characteristics and possible outcomes of a healthcare intervention may vary. Some patients may have a preference for maximising the probability of response to treatment, whereas others may have a preference for avoiding certain adverse events. Some patients may have a preference to avoid injections, whereas others may wish to avoid frequent oral medication.
However, the standard decision rules for cost-effectiveness analysis identify a single technology as maximising expected health benefit. Decisions made on this basis may fail to recognise variation in preferences and circumstance and be economically inefficient failing to maximise patient utilities, compromise treatment adherence, and worsen health inequalities.
In this issue panel we will explore how heterogeneity in individual preferences might be investigated using qualitative and quantitative methods, including elicitation of personal utility functions, and accounted for in decision-making in terms of deliberation and ‘decision-rules’.
OVERVIEW: Ben Van Hout will discuss evolving methods to solicit more accurate estimates of utility weights using personal utility functions and their potential use in decision-making.
Neil Hawkins will moderate and will provide context by exploring how the ‘rules’ of cost-effectiveness analysis might be adapted to account for preference heterogeneity including the role of cost-minimisation, net-benefit, and ‘individualised cost-effectiveness” approaches.
Shelagh Szabo will describe methodologies that have been used to explore heterogeneity in patient preferences. These include qualitative methods using grounded theory and quantitative methods such as discrete choice experiments with latent class analysis.
Amanda Adler, as a clinician and an ex-chair of a NICE technology appraisal committee, will moderate and share her experiences in considering heterogeneity in preferences in real-world decision making. We will use real-time polling to allow the audience to express their own heterogeneity in preferences with respect to this topic and to allow the audience to act as a reimbursement ‘decision-maker’.
Amanda Adler, MD, PhD, FRCP
Churchill Hospital, Oxford, Oxford, United Kingdom
Neil Hawkins, MSc, PhD, MBA, CStat
University of Glasgow, Glasgow, United Kingdom
Shelagh Szabo, MSc
Broadstreet Health Economics & Outcomes Research, Vancouver, BC, Canada
Ben A Van Hout, PhD
Sean Sullivan, BScPharm, MSc, PhD
CHOICE Institute, University of Washington, Seattle, WA, USA
Jens Grueger, PhD
Boston Consulting Group, Zürich, Zürich, Switzerland
Ansgar Hebborn, PhD
F. Hoffmann-La-Roche AG, Basel, BS, Switzerland
Inma Hernandez, PharmD, PhD
University of California San Diego, La Jolla, CA, USA
ISSUE: Creating educational content on the HTAR for patients while the implementation is still ongoing.
Maria Dutarte, MS
EUPATI, Utrecht, Netherlands
Lotte Klim, EUPATI Fellow
EUPATI, Copenhagen, 84, Denmark
Finn McCartney, BA, MA
EUPATI, Amsterdam , NH, Netherlands
Mitchell Silva, PhD
EUPATI Belgium vzw, Brussels, Belgium
Julian Shepelev, MSc
Baxter Healthcare, London, LON, United Kingdom
10:15AM - 10:30AM
Description: The UK Government’s 2019 national action plan for AMR committed to testing an incentive for companies to invest in new antimicrobials. To meet this commitment, NICE and NHS England ran a pilot using a world-first approach that pays companies a fixed annual fee for antimicrobials regardless of volumes used. Payment was based on a bespoke NICE health technology assessment, using quantitative health economic analyses to estimate population-level benefits in quality-adjusted life years for 2 drugs. Innovative modelling incorporated public health benefits, used in vitro data as a surrogate for clinical outcomes, and projected AMR trends using national surveillance data. Stakeholders from the pharmaceutical industry, academia and clinical practice, and international key opinion leaders, participated in workshops to review lessons learned.
Lessons Learned: The pilot demonstrated feasibility of delinking payments from volumes within public procurement systems, and produced the most comprehensive valuation of antimicrobial products to date. Net health benefit estimates justify paying the UK’s ‘fair share’ towards antimicrobial research and development. But modelling antimicrobial value is resource-intensive and subject to high uncertainty. Collectively, these learnings have led to a more pragmatic approach to determining contract value: a multi-criteria decision analysis (MCDA) tool, which NICE and NHSE have co-developed (informed by drivers of value identified in the pilot, validated by clinical experts from the UK Government, and subjected to public consultation in 2023). It will be implemented in a UK-wide Antimicrobial Product Subscription Model from summer 2024, using a single procurement process across regional healthcare systems. It is hoped this approach will be scalable and adaptable in other countries, to collectively achieve a meaningful incentive for global investment in antimicrobials.
Stakeholder Perspective: Payer, government
11:00AM - 11:15AM
Description: This case study analyses the cumulative impact of NHS-industry partnerships on healthcare decision-making and outcomes, using real-world evidence (RWE). The study examines data from secondary and primary care levels across the UK, focusing primarily on cardiovascular disease and type 2 diabetes management. Findings indicate that secondary care trusts with partnerships align more closely with HTA-approved prescribing guidelines, leading to a 59% stronger correlation between increased prescribing and improved patient outcomes. Collaborating trusts saw a reduction in hospital stays, equating to 530,000 fewer bed days and annual savings of £185 million. In primary care, collaborations enhance disease management, with a 63% improvement in blood glucose control among frail diabetic patients and a 28% greater improvement in cardiovascular disease control in collaborating Primary Care Networks compared to the national average.
Lessons Learned:
Stakeholder Perspective:
Commissioners and Providers: Benefit from improved resource use, cost savings, and enhanced patient care.
Patients: Gain access to effective treatments and improved health outcomes.
Industry: Gains evidence of the impact of their products and partnerships, supporting innovation and adoption.
Government and Policymakers: Obtain valuable insights into the benefits of cross-sector collaborations, guiding future healthcare policies.
This study underscores the importance of sustained NHS-industry partnerships, advocating for their expansion as a tool to achieve better patient outcomes, operational efficiencies, and support for the UK's life sciences sector.
10:30AM - 10:45AM
Description: Evaluations of The Welsh Governments’ Long COVID service, a child wellbeing and mental health programme, and Cardiff & Vale UHB Orthopaedics’ Rehabilitation Model have used SROI. Commercial alternatives were used as financial proxies and the wellbeing valuation technique (Fujiwara 2014) enabled alternative valuations of outcomes. An SROI is also being applied to the Paediatric Integrated Care Clinic pilot, with the hope to inform service expansion across Wales.
Lessons Learned: Choosing the most appropriate valuation technique to adequately represent gathered outcomes was a challenge across all projects. Different wellbeing techniques can have vastly different monetary values due to subjectivity in how they are applied, and therefore need to be interpreted with caution. A more standardised approach of SROI valuation for health service evaluations could reduce the risk of misinterpretation and increase comparability.
Careful consideration should be given to researcher interactions and participant burden. Many participants are vulnerable, face barriers to healthcare or are “over surveyed”, risking low response rates or inaccurate data. Considering directed data collection methods and use of routine data (e.g., PROMs) could reduce this burden. However, standardised data collection tools may not ask patients about what matters most to them.
Stakeholder Perspective: SROI methodology is concerned with directly capturing societal and stakeholder perspectives (primarily patients). Decision-makers have used the SORI results to inform continued funding and commissioning of new services, in part due to the accessible nature of results, and aligning with social value policy agendas.
10:45AM - 11:00AM
Description: We collect information on interventions funded from settlement dollars to systematically measure the heterogeneity of funding uses, and the impact of these interventions on key population health outcomes of interest, such as trends in overdose deaths, trends in diagnosis of opioid use disorder, etc.
Lessons Learned: This study uses empirical data to assess the effectiveness of a strategy to allow local autonomy over the decision on interventions to fund from settlement funds in order to reduce the harm caused by opiates. To date over $72M USD has been spent and committed on remediation programs by all 67 counties in Pennsylvania. Our research characterizes the variation in spending by type of intervention (e.g., prevention, acute care treatment, education, provider training, etc.) and the populations targeted by the spending (e.g., criminally justice involved individuals, post-partum women, at risk youth, etc.). We use statistical techniques to associate changes in key population health measures to the interventions, with the goal of assessing real world effectiveness.
Stakeholder Perspective: Our work has implications for how best to structure and allocated damage awards to address important population and public health outcomes.
OBJECTIVES: The Guangdong-Hong Kong-Macao Greater Bay Area is a pioneer for air pollution control in China. This study investigated the health and economic benefits of air pollution control measures in Shenzhen, which has made great achievement in air quality improvement, aiming to empirically demonstrate the health and economic significance of air pollution control.
METHODS: The pollutants PM2.5 and O3 were selected as the main focuses of our analysis, and the proportional hazards model based on Poisson regression, the statistical life value, the cost of illness and the willingness-to-pay method were all employed to assess the health-economic benefits of air pollution control from 2014 to 2022, and to further quantify the impacts of short-term public health policies on air quality and social health-economic burdens.
RESULTS: Prior to COVID-19, health hazards and economic burdens caused by PM2.5 declined significantly as air quality improved, as premature deaths reduced by a total of 1,759 (95% Cl: 867-2,624) and $27.97 million (95% Cl: $13.89-41.39 million) was saved in economic costs. However, the health hazards and economic burden caused by O3 had shown a fluctuating upward trend. During the COVID-19 pandemic, the Chinese government had implemented strict lockdown policies, which indirectly reduced health hazards and economic burdens caused by various pollutants.
CONCLUSIONS: The effective implementation of air pollution control policies can help reduce health hazards and health economic burdens. Governments should recognize the effects and consequences of public health policy implementation, and strengthen the transparency and effectiveness of public health policymaking, thus safeguarding ecological security of China and the world.
OBJECTIVES: Chronic kidney disease (CKD) is a leading cause of morbidity and mortality, affecting over 840 million worldwide. Switzerland’s prevalence is 10.9%, impacting one in four over-60-year-olds. Renal replacement therapy (RRT) constitutes a significant proportion of CKD-related healthcare costs, despite utilization by only 1% of Switzerland’s CKD population. Anticipated increases in CKD patient numbers necessitate strategic healthcare planning. To inform public health strategies, this IMPACT-CKD study aims to project clinical, economic, societal and environmental burdens of CKD in Switzerland.
METHODS: The IMPACT-CKD patient-level simulation model was utilized to project CKD-progression and outcomes in the Swiss population over a 10-year horizon. Individuals were assigned CKD-relevant characteristics – including kidney function measures, metabolic and cardiovascular comorbidities and events – based on real-world data (national statistics, registries and databases). Patients progressed through the disease, diagnosed or undiagnosed, following categorisation into non-CKD or one of six CKD stages. Inputs and results were validated and calibrated extensively via published literature, real-world evidence and expert consultations.
RESULTS: Between 2022 and 2032, IMPACT-CKD projects, among others, Switzerland’s cases of CKD to rise by 6.7% (from 0.96m to 1.03m). Prevalent counts for Stages 3 to 5 and RRT will surge by 18.1% (from 437k to 516k) and 57.2% (from 8.4k to 13.2k) respectively. CKD-related healthcare costs (including RRT) will increase by 27.6% (from CHF 3.3b to CHF 4.2b) to 4.6% of Swiss healthcare spending in 2032. RRT-associated costs will rise by 77.3% (from CHF 424m to CHF 752m). Over 10 years, CKD will incur CHF 12.9b in lost productivity, 42.8m missed workdays and CHF 606m in lost tax revenue. CKD's carbon footprint will increase by 13% (from 230m to 259m kg CO2 equivalents).
CONCLUSIONS: IMPACT-CKD projects major increases in the clinical, economic, societal and environmental burdens of CKD on Switzerland. Comprehensive health policies and strategic planning are needed urgently to mitigate impact.
OBJECTIVES: Healthcare delivery carries an environmental footprint; in 2019, global warming emissions from healthcare were estimated to be 4.4% of all anthropogenic emissions. Better care can lead to better health outcomes as well as a lower environmental footprint. This study evaluates the potential impact of effective prevention and management of Type 2 Diabetes Mellitus (T2DM) on both clinical outcomes and carbon emission in the UK healthcare system.
METHODS: We incorporated an environmental module into the existing IQVIA Core Diabetes Model® (CDM) to estimate the impact on CO2 equivalent emissions associated with T2DM clinical outcomes over a 50-year time horizon to assess lifetime impacts. We conducted two hypothetical scenarios: 1) preventing pre-diabetes progression to T2DM through diet and exercise versus no intervention and natural disease progression to diabetes; 2) well-controlled T2DM patients (HbA1c < 7%) on interventions with clinical benefit of preventing interconnected disease like chronic kidney disease (CKD) and cardiovascular disease (CVD) versus uncontrolled patients (HbA1c of 8.5 - 9%).
RESULTS: In scenario 1, preventing progression to T2DM led to 6.36 additional undiscounted life years with 67% lower CO2e emissions per patient over lifetime, compared to naturally progressed patients (emissions of 9,586 kg over 37.12 years versus 28,716 kg CO2e over 30.76 years, respectively). In scenario 2, well-controlled T2DM with management of CVD and CKD risk led to 2.18 additional undiscounted life years with 23% lower CO2e emissions per patient over lifetime compared to uncontrolled T2DM (emissions of 14,261 kg over 23.10 years versus 18,624 kg CO2e over 20.92 years, respectively). In both scenarios, the reduction in CO2e emissions was primarily due to reduced emissions related to treating CVD, renal, and eye diseases.
CONCLUSIONS: Effective prevention through diet and exercise and management of T2DM through adoption of evidence-based guidelines can improve patient outcomes and reduce the healthcare-related environmental impact.
OBJECTIVES: Patient travel accounts for 5 % of the healthcare sector’s carbon footprint. Cataract surgery is the most commonly performed surgical procedure worldwide. Its systematic environmental optimization is essential to reach the decarbonization goals of international healthcare systems. In this study, we aim to analyze the environmental effects of patient travel for cataract surgery in an ophthalmology center in Bavaria, Germany.
METHODS: We analyze all cataract surgeries conducted on October 23, 2023. We quantify greenhouse gas (GHG) emissions due to patient travel for cataract surgery and follow-up care within three months of surgery. In addition, we analyze the environmental effects of running branch practices for follow-up care.
RESULTS: 28 patients had cataract surgery. All patients traveled by car. Average one-way travel distance was 33.6 miles. Average GHG emissions were 16.4 kg per patient. In addition to GHG, 0.102 kg carbon monoxides, 0.016 kg volatile hydrocarbons, 0.041 kg nitrogen oxides, and 0.0006 kg particulates were emitted per patient for travel to and from cataract surgery. Environmental costs are estimated at 5.46 EUR per patient according to German Federal Environmental Agency's estimate of 195 EUR per ton of carbon dioxide equivalent. Patients returned for one to four follow-up visits. 50 % of patients traveled to the main surgery center, while 50 % used one of six branch practices for follow-up care. Analysis of all patient files revealed a total of 2779.8 miles of actual patient travel for follow-up care. Compared to an outpatient specialist care structure without branch practices, 768.4 kg of GHG could be saved, i.e. GHG emissions were reduced by 42.5 %. Total environmental costs of patient travel for 28 surgeries and follow-up care were 378.54 EUR.
CONCLUSIONS: Branch practices substantially reduce the environmental impact of patient travel. The carbon footprint of cataract surgery would benefit from digital alternatives for follow-up care.
OBJECTIVES: Multilevel Network Meta-Regression (ML-NMR) is an advanced method that facilitates estimation of relative treatment effects in the target population as a function of patient characteristics and can be extended to networks of any size, thus overcoming limitations of common indirect treatment comparison (ITC) approaches including matching-adjusted indirect comparisons (MAICs). We assessed the performance of ML-NMR versus MAIC in reducing biases in an ITC using a simulated time-to-event dataset.
METHODS: We simulated hypothetical individual patient dataset (IPD) for Trial-AB (treatment B vs A) and aggregated dataset (AgD) for Trial-AC (C vs A) including both continuous and dichotomous covariates. Following the methods from Phillippo et al, integration points were estimated using multinma package, and survival times were modelled with Weibull, exponential, and Gompertz distributions. The predictive performance of each model was assessed using the leave-one-out information criterion (LOOIC), with lower values indicating better predictive performance. Treatment effects were predicted to AgD population. For MAIC, we adjusted IPD from the Trial-AB to match the AgD of Trial-AC. Weighted and unweighted parametric ITC were used to derive hazard ratios (HRs) and 95% confidence intervals (CIs).
RESULTS: The Weibull model showed the best fit (LOOIC: -422.27). No divergent transitions were observed. For MAIC, estimated HR was 0.62 (95%CI: 0.47-0.82) for B vs C, while ML-NMR predicted HR of 0.43 (95% Credible interval [CrI]: 0.38-0.49) in AgD. A variation in point estimate was observed due to significant effect modification observed in ML-NMR. Direction of estimates were in favour of treatment B in both MAIC and ML-NMR, but ML-NMR reduced uncertainty compared to MAIC by explaining within-trial variation due to effect modification.
CONCLUSIONS: We recommend using ML-NMR for population adjustment in ITCs of time-to- event outcomes as it generates precise estimates over MAIC and can be used to predict estimands between any pair of treatments in the desired target population.
OBJECTIVES: Multivariate network meta-analysis (MVNMA) is an effective method for incorporating correlation information among multiple outcomes in network meta-analysis, potentially improving the precision of the treatment effect estimation. However, the correlation information typically cannot be extracted from summary statistics reported in individual papers, and plausible values are often extrapolated from external evidence. If the extrapolated values are biased, the resultant MVNMA evidence can be seriously biased. This issue is one of the main reasons why MVNMA is rarely adopted in practice. In this article, we propose a new method to restore the correlation information using only summary statistics reported in individual papers under a certain condition and illustrate its practical utility through an application to a comparative effectiveness analysis for plaque psoriasis.
METHODS: We provide an unbiased estimation method for correlation coefficients between multiple outcomes in MVNMA when the multiple outcomes have nested structures, i.e., events of one outcome variable overlap with those of other outcome variables. In the plaque psoriasis study, the three outcome variables, 100%, 90%, and 75% reductions in their Psoriasis Area and Severity Index scores, had nested structures, and all correlation information was accurately restored from the summary statistics. We then conducted MVNMA using the correlation information and evaluated the improvements in statistical precision compared with the standard univariate network meta-analyses (NMAs).
RESULTS: MVNMA provided more precise treatment effect estimates compared with standard NMAs in general. In particular, for the outcomes infrequently reported in individual trials (formally regarded as "missing" in MVNMA), the precision of treatment effects was markedly improved.
CONCLUSIONS: Although MVNMA has rarely been applied in practice due to the difficulty of estimating correlations, our new method effectively addresses this problem. Utilizing this new approach, MVNMA can provide more precise and unbiased comparative effectiveness estimates.
OBJECTIVES: Conducting indirect treatment comparisons (ITC) between novel histology-independent therapies (HIT) assessed in basket trial settings presents a unique challenge. Basket trials of HITs enroll patients with a targeted mutation or biomarker regardless of their tumour histology and usually lack a concurrent control arm. As histology may be prognostic for the outcomes of interest, failure to adjust for imbalances in the composition of histologies between comparators could confound treatment effect estimates when performing an ITC. However, the severely limited sample sizes in typical basket trials can make it difficult or infeasible to conduct a traditional population-adjusted indirect comparison.
METHODS: We implement a novel variant of a Bayesian hierarchical model (BHM) ITC approach for unanchored comparison of two treatments assessed in separate basket trials. The model mitigates risk of confounding due to differences in histology composition by incorporating modelling for heterogeneous responses by histology. This approach allows for partial pooling of information, assuming exchangeability in (i) absolute response across histologies and (ii) relative treatment effects across histologies. We apply the modelling approach to an ITC of larotrectinib vs. entrectinib for NTRK-fusion-positive solid tumours for an overall response rate endpoint.
RESULTS: We find that the posterior probability of superiority in response for larotrectinib exceeds 80% for all tumour types considered, ranging from a low of 80.9% for hepatocellular tumours to a high of 95.1% for thyroid tumours. However, these estimates fall short of conventional thresholds for statistical significance.
CONCLUSIONS: Our analysis provides some evidence that larotrectinib may yield superior response compared to entrectinib for multiple types of NTRK-fusion-positive solid tumours after adjusting for differences in histology composition. However, this relies on potentially strong exchangeability assumptions, and the possibility that residual confounding remains cannot be ruled out. Nonetheless, use of BHMs provides a potential way forward for the particularly data-scarce setting of conducting ITCs for basket trials.
OBJECTIVES: Real-world external control arms (RW-ECAs) often represent the primary source of comparative evidence in HTA submissions where pivotal trials are single arm. RW-ECA evidence is associated with increased uncertainty versus randomized controlled trials, driven primarily by potential unmeasured/residual confounding and a perceived ability to influence results through the choice of analytical approaches. The National Institute for Health and Care Excellence (NICE) RWE framework outlines sensitivity analyses to explore this uncertainty. This study reviews current practices in the application of these approaches in NICE submissions utilizing RW-ECAs.
METHODS: RW-ECAs submitted to NICE were identified through screening company submissions of all single technology appraisals with draft guidance from 2019-2024. Data were extracted on sensitivity analyses examining the impact of various approaches used to account for observed sources of bias (e.g., data curation decisions, adjustment method), approaches for assessing residual confounding (e.g., quantitative bias analysis (QBA), negative control outcomes), and on payers’ views regarding residual uncertainty.
RESULTS: RW-ECAs were identified in 18/267 submissions. 14/18 submissions examined the impact of different approaches to adjust for observed bias, including through applying different inclusion/exclusion criteria (4/18), adjustment methods (8/18), outcome definitions (1/18), and comparators (1/18). One study estimated both the average treatment effect and average treatment effect on the treated. Methods to assess the impact of unmeasured confounding were used in only 2/18 submissions and applied only simple forms of QBA. The Evidence Review Group (ERG) highlighted the risk of residual confounding in 9/18 submissions. In 2 submissions, the ERG specifically highlighted systematic differences in eligibility criteria between treated and control arms, and in one case explicitly criticized the company for not estimating residual bias.
CONCLUSIONS: Although formal methods are recommended for exploring uncertainty in RW-ECA evidence, these are not being applied in practice. Increased use of these methods may result in increased acceptability of RW-ECA evidence in HTA.
PURPOSE:
DESCRIPTION:
Joe Hollinghurst, PhD, MSc, BSc
HEOR ltd, Cardiff, CRF, United Kingdom
Nia C Jenkins, MSc
Health Economics and Outcomes Research Ltd., Cardiff, United Kingdom
Alik Vodyanov, MSc
HEOR ltd, Cardiff, Cardiff, United Kingdom
Gemma Taylor, PhD
University of Bath, Bath, Avon, United Kingdom
Mitchell Higashi, PhD
ISPOR, Lawrenceville, NJ, USA
Rachael Fleurence, PhD
NIH, Washington, DC, USA
Ganhui Lan, PhD
Pfizer, New York, NY, USA
IMO Health, Rosemont, IL, USA
Sylwia Bujkiewicz, PhD, MSc
University of Leicester, Leicester, United Kingdom
Georgios Nikolaidis, PhD
IQVIA, Sheffield, United Kingdom
Anastasios Papanikos, PhD
GlaxoSmithKline, Stevenage, UK, United Kingdom
Janharpreet Singh, PhD
University of Leicester, Leicester, LCE, United Kingdom
Live
We will then zoom in on secondary use, setting out the EHDS model for Health Data Access Bodies, Secure Processing Environments, the role of single data holders, the rights to citizens and the duties of data holders and researchers. In this we will outline what is already set in the Regulation, and the gaps which are still to be addressed in secondary legislation.
Following this 15-minute introduction we will open a polling asking people to rate which aspects of the new EHDS they believe are most worrying, and where they would like to push their country and the EU to take particular action. Based on the voting we will share background information on what we see emerging in Brussels and beyond to address the problem issues voted most problematic (a number of candidate issues will be shown on a slide). In order to avoid a negative focus, we will then asks for votes on the most positive aspects, and again provide Brussels commentary on those issues.
We will close the session with a final vote in which we ask participants to rate their current access to data for secondary research from 1-10 and then to predict how the EHDS may impact this number.
Katja Murray, Master
FTI Consulting, Copenhagen, 84, Denmark
Petra Wilson
Health Connect Partners, FTI Consulting, Brussels, Belgium
PT1 Decision-Making in Ireland: Utilizing Machine Learning to Build Predictive Algorithms That Assess Words Contributing to NCPE Decision Making on Reimbursement
PT2 Real-Life Benefit-Risk Assessment of Medical Products Using Bayesian Multi-Criteria Augmented Decision Analysis (MCADA) in Oncology
PT3 Using the NMAi Tool to Ease Comparative Analyses of HTAs Among the Nordics
PT4 Truncating Time-to-Event Data for Methodological Investigation
PT5 Structural Uncertainty in Evidence Synthesis: A Case Study Applying Model Averaging in Bayesian Multi-Level Metwork Meta-Regression
PT6 An “R-Shiny” Interface Designed As a One-Stop Solution for All Kinds of Survival Analysis According to NICE Technical Support Documents 14 and 21
PT7 Reassessment Dynamics of Orphan Drugs in Germany’s Benefit Assessment Process
PT8 Access to Orphan Medicines in Ireland
PT9 Drivers of Access to Gene Therapies in Poland and Selected Central and Eastern Europe Countries
PT10 Real-World Management of Patients With Immune Thrombocytopenia in Italy: A Retrospective Database Study
PT11 Authoring of Peer-Reviewed Articles on the Experiences of Patients With Rare Diseases by Patients and Their Caregivers: A Rapid Review
PT12 Perceptions of Rare Disease Patients and Their Caregivers Regarding Access to Disease Information and Interactions With Their Healthcare Providers: Results From an International Survey
The panel will explore the foundations and advances in causal machine learning methods, focusing on deriving patient insights and improving treatment decision-making.
Dr Katja Hakkarainen will provide the epidemiological perspective on the causal inference methods, especially the adoption of the target trial emulation framework in observational research. She will discuss the principles of target trial emulation, motivations for using the framework, and examples of applications of the framework in observational research.
Dr Andy Wilson will provide an overview of the statistical science and implementation of casual inference and describe emerging machine learning-based methods, such as targeted maximum likelihood aimed at addressing confounding. He will also present a case study on estimating vaccine effectiveness from observational data to illustrate the applications of these methods. He’ll conclude with an outline of promising innovative dynamic methods (including the concept of the modifiable ‘treatment policy’).
After this session, it is anticipated that participants should be able to:
Parexel
Jackie Vanderpuye-Orgle, PhD
Parexel International, Billerica, MA, USA
Katja M Hakkarainen, PhD
Parexel International, Gothenburg, Sweden
Andy Wilson, PHD, MStat
Parexel International, Waltham, MA, USA
The aim of this symposium is to examine the barriers to using existing age-appropriate tools for HTA in children and adolescents, to identify methods gaps that need to be addressed, and to explore ways that pediatric evidence-based decision-making can be strengthened. The symposium will present a range of perspectives, with speakers from HTA, industry, and academia.
Prof. Devlin will introduce the speakers and provide background for their presentations by describing some of challenges and recent advances in the pediatric PRO and HTA field. Dr. Shah will then talk about the issues faced by decision-makers in organisations such as NICE who have to deal with the reality of basing decisions on sometimes less-than-optimal evidence, and ways of addressing those difficulties, while Dr. Knies will describe efforts made by the National Health Care Institute of the Netherlands to extend formal product appraisal to pediatric health care. Dr. Chandler will provide an industry perspective on the challenges involved in assembling robust evidence in support of pediatric health-care products, and Prof. Ungar will explore ways in which the field could advance from a methodological perspective. Prof. Devlin will lead a Q&A session with the panel of speakers; questions and comments from the audience are welcomed.
EuroQol Research Foundation
Nancy Devlin, PhD
School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
Fleur Chandler, MSc
Sanofi and Patient Advisory Board Lead, Duchenne UK, Reading, Berkshire, Great Britain
Koonal Shah, PhD
Wendy Ungar, MSc, PhD
The Hospital for Sick Children Research Institute, Toronto, ON, Canada
This session will stimulate, within the ISPOR community, a discussion of potential policy changes that HTDs and payers could make to better use global differential pricing to improve access to innovative medicines in LMICs.
Mikkel Oestergaard, PhD
MSD Innovation & Development GmbH, Zürich-Flughafen, Switzerland
Yaneth Giha, MA
FIFARMA, Bogota, Bogota, Colombia
Kärt Veliste, BSc; MSc
Estonian Health Insurance Fund, Tallinn, Estonia
HTA submissions typically use generic frameworks for assessing medical innovations, which are not specific for diagnostics or medical devices, and may hinder how their value is measured and evaluated. Additionally, these frameworks may not include RWE, which is important to enable a complete evaluation of evidence, particularly for medical devices.
This forum aims to highlight opportunities for improvements to HTA frameworks to more fully capture the value of diagnostics and medical devices. Case studies will be used to illustrate uses of RWE and the importance of including a tailored evidence evaluation framework for diagnostics and medical devices. The forum will also include a state of the art discussion on the methods to evaluate diagnostic tests (Dr. Lavinia Ferrante), a presentation on the current challenges experienced by HTAs for the assessment on these technologies (Mr. David Tamblyn), and a discussion with two medical device companies (representatives: Lindsay Bockstedt and Chiara Capobianco) presenting their approaches in using RWE, and sharing how evidence standards may evolve as RWE becomes more widely accepted. The panel will conclude with questions from the moderator and the audience.
Arturo Cabra, MSc
GE Healthcare, Miami, FL, USA
Lindsay Bockstedt, PhD
Medtronic, Minneapolis, MN, USA
Chiara Capobianco, MSc
Boston Scientific, Milan, Milan, Italy
Lavinia Ferrante di Ruffano, PhD
York Health Economics Consortium, York, United Kingdom
David Tamblyn, MPH
The University of Adelaide, Adelaide, SA, Australia
However, developments in HTA methodologies may usually not match the needs of HTA bodies. How do we better align HTA needs to academic HTA methods development? In this session, we will introduce a new EU Horizon Europe-funded project, SUSTAIN-HTA, that aims to bring academic developments in HTA methodology closer to the HTA needs. Two representatives from NICE and Central and Eastern Europe will indicate the need for such a project. On the other hand, a representative from NICE, a well-established HTA body from the United Kingdom, will defend the position that the current HTA methods are sufficient to address all the HTA needs and that the main issue is that we need better data and RCTs from the health technology developers (HTDs).
Wim Goettsch, PhD
National Health Care Institute (ZIN); Utrecht University, Division of Pharmacoepidemiology and Clinical Pharmacology, Diemen, Netherlands
Dalia Dawoud, PhD
National Institute for Health and Care Excellence, London, LON, United Kingdom
Zoltan Kalo, MD, PhD, DrHabil
a) Semmelweis University; b) Syreon Research Institute, Budapest, Hungary
Oresta Piniazhko, PhD
State Expert Centre of the Ministry of Health of Ukraine, Kyiv, Ukraine
Adelphi Values PROVE™
Anne Marciniak, PhD, MSc, MPhil
Adelphi Values PROVE™, Bollington, United Kingdom
Danielle Riley, MSc
WEP Clinical
Heather Masters, BSc (Hons)
WEP Clinical, London, United Kingdom, United Kingdom
Manuel Antonio Espinoza, MD, MSc, PhD
Pontificia Universidad Católica de Chile, Santiago, Chile
Rafael Pinedo-Villanueva, PhD
University of Oxford, Oxford, OXF, United Kingdom
Kanya Shah, PhD, PharmD, MBA, MS
University of Illinois Chicago College of Pharmacy, Chicago, IL, USA
Gihan Hamdy Elsisi, PhD
HTA office, Cairo, Egypt
Fang-Ju (Irene) Lin, PhD
Department of Pharmacy Systems, Outcomes and Policy, University of Illinois Chicago College of Pharmacy, Taipei City, Taiwan
Assessing the measurement characteristics in a given database is time-consuming and expensive. We highlight approaches to expediting data validations through stratified batch validation, through LLM support in establishing electronic health record (EHR)-based reference standards, and through claims diary reviews. To what extent can coding algorithms be shared across data sources and how can their transportability be assessed? We connect our points with recent FDA/EMA guidance documents, discuss implications for federated data analyses, how to prepare decision-makers as reviewers of EHR/claims data analyses, and how to improve your marketing position as a producer of RWD.
Chief, Brigham and Women's Hospital, Boston, MA, USA
Daniel Alhambra, MD, PhD
Oxford University, London, OXF, United Kingdom
Megan John, MBBS, BSc, MRCGP
National Health Service and Chair of NICE Appraisal Committee D, Frimley, Surrey, United Kingdom
Federico Felizzi, PhD
Menarini GmbH, Basel, BS, Switzerland
University of Sheffield & Delta Hat Limited, Sheffield, United Kingdom
Melanie Whittington, PhD, MS
Tufts Medical Center (CEVR), Boston, MA, USA
Grace Hampson, MSc
Tom Denee, PharmD, MBA
Johnson & Johnson’ Innovative Medicine Europe, Zeist, Netherlands
Scott Ramsey, MD, PhD
Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
David Sánchez González, BA
Retinosis FARPE, Madrid, Madrid, Spain
Hugo Pedder, PhD, MSc, BSc
University of Bristol, Bristol, BST, United Kingdom
Mark Harries, BPharm (Hons), MSc, MRPharmS
Ipsen Ltd, London, United Kingdom
University of California – San Francisco, San Francisco, CA, USA
Dawn Lee, MMath, MSc
University of Exeter, Exeter, United Kingdom
Ramiro Gilardino, MD, MHS, MSc
MSD, Dubendorf, ZH, Switzerland
Jag Chhatwal, PhD
Harvard Medical School and Value Analytics Labs, Wilmington, MA, USA
Diana Delnoij, PhD
Dutch National Health Care Institute, Diemen, Netherlands
Ipek Ozer Stillman, MS, MBA
Takeda Pharmaceuticals, Cambridge, MA, USA
2:30PM - 2:45PM
OBJECTIVES: It is recognised that unpaid caregiving can affect health-related quality of life (HRQoL). It is also understood that using EQ-5D-5L instead of EQ-5D-3L to measure patient HRQoL shifts mean utility scores up the scale towards full health and compresses them into a smaller range.
METHODS: We used causal inference methods in our cross-sectional observational UK dataset of 3,536 carers and 39,901 non-carers. We conditioned on potential confounders: sex, age, family status, education, and diagnosis of a health condition. We used inverse probability weighting with a multinomial logit model to predict the probability of belonging to the weekly hours of caregiving intensity classes. We predicted EQ-5D-3L and -5L index scores using regression and the probability of reporting full health using a probit model. We checked for balance and overlap.
RESULTS: People providing upwards of 5 hours/week unpaid care had statistically significantly worse EQ-5D-3L and 5L index scores than non-carers. The average treatment effect was smaller for all intensities using 5L than 3L: a maximum of -0.157 for 50+ hours for 3L compared to -0.109 for 5L. Carers were less likely to report full health for 3L and 5L and the effect size was bigger for 3L: up to -0.165 compared a maximum of -0.133 for 5L.
CONCLUSIONS: Unpaid carers have worse HRQoL than non-carers and the utility decrement increases as intensity of care increases. Using EQ-5D-5L instead of EQ-5D-3L reduces the utility decrement associated with caregiving and reduces the decrease in the proportion of respondents reporting full health. Using EQ-5D-5L instead of EQ-5D-3L will reduce the cost-effectiveness of interventions that decrease the caregiving burden, all other things being equal.
2:00PM - 2:15PM
OBJECTIVES: For healthcare treatments to be approved for use in the UK NHS, robust evidence is required of their impact on health-related quality of life (HRQoL). The National Institute for Health and Care Excellence (NICE) recommends the use of the EQ-5D. Concerns have been raised about the responsiveness of the EQ-5D in the context of multiple sclerosis (MS), however empirical evidence is limited. This research aims to assess the responsiveness of the EQ-5D-3L compared to an MS-specific utility measure, the Multiple Sclerosis Impact Scale Eight Dimensions (MSIS-8D), in people with MS.
METHODS: Analysis was undertaken using data from the UK MS Register (UKMSR), a prospective, longitudinal, cohort study with over 10,000 participants. We investigated changes in EQ-5D-3L and MSIS-8D values before and after (i) commencement of disease-modifying treatment and (ii) onset of new symptoms; and (iii) between time-points with/without a contemporary relapse. Responsiveness was evaluated using standardised response means (SRM), standardised effect sizes (SES) and paired t-tests.
RESULTS: Neither measure appeared responsive to DMT initiation (ie p > 0.05). Both measures were responsive to experiencing a relapse, with the MSIS-8D (mean change -0.047, p = 0.0001, SRM -0.330, SES -0.228) showing greater responsiveness than the EQ-5D (mean change -0.047, p = 0.0054, SRM -0.226, SES -0.139). Neither measure was responsive to experiencing new symptoms (EQ-5D SRM -0.095, SES -0.070; MSIS-8D SRM -0.138, SES -0.118), although the reduction on both measures was statistically significant (p<0.05). In all analyses, 95% confidence intervals for the SRMs indicated that the MSIS-8D was significantly more responsive than the EQ-5D-3L.
CONCLUSIONS: Although the MSIS-8D appears to offer greater responsiveness compared to the EQ-5D-3L, the responsiveness of both measures in this analysis was limited. This adds weight to existing concerns about the ability of QoL measures used in healthcare decision-making to fully capture treatment effects in MS.
2:15PM - 2:30PM
OBJECTIVES: We compared the measurement properties of two different proxy versions of EQ-5D-Y-3L (Y-3L) and EQ-5D-Y-5L (Y-5L) in caregivers of paediatric patients with asthma or eczema. Using the proxy-proxy version (P1), caregivers rate the child’s health from their (the proxy’s) point of view; using the proxy-patient version (P2), the caregivers are required to imagine how the child would rate their own health.
METHODS: We recruited paediatric asthma/eczema patients and their parents/legal guardians from the specialist outpatient clinics of two tertiary hospitals in Singapore. Patients completed Y-3L and Y-5L. Proxies completed either the P1 or P2 version of Y-3L and Y-5L. Asthma control was assessed using the Global Initiatve for Asthma (GINA) assessment while eczema severity was assessed by the clinicians using the validated Investigator Global Assessment for Atopic Dermatitis (vIGA-AD). A subset of patient-proxy dyads was reassessed after 101.3 ± 63.6 days. Known-groups validity, responsiveness to improvement, and patient-proxy agreement were assessed for both proxy versions of Y-3L and Y-5L using Y-3L index score (3L-Index), Y-5L level sum score (5L-LSS), and EQ VAS.
RESULTS: We included 328 child-proxy dyads (P1: 154 dyads; P2: 174 dyads). P2 demonstrated lower ceiling effects [P1: 53.90%-63.90%; P2: 47.13%-56.32%]. All P1-derived scores differed as expected between known groups defined by GINA or vIGA-AD but not all P2-derived scores differed as expected. P1/P2 standardized effect sizes for improvement were 0.48/0.21 for 3L-Index and -0.28/-0.42 for 5L-LSS. Patient-proxy agreement was low-to-moderate in both proxy versions (ICC range: 0.22-0.42). All proxy-reported scores indicated better health as compared to self-reported scores regardless of which proxy version was used.
CONCLUSIONS: In this population, neither proxy version was superior on the psychometric tests performed. Either version may be suitable for use depending on the purpose of the study they are to be used in. Further research is needed to confirm these results.
1:45PM - 2:00PM
OBJECTIVES: Health technology assessment (HTA) in the UK often requires that health-related quality of life is considered in a cost-utility analysis (CUA). Most studies contain missing data at some level. Missing utility data can misrepresent the denominator of the ICER. Despite this, there are no definitive guidelines on how to manage missingness for UK HTA, and analysts rely on judgment to address missingness. We intend for this research to formalise our recommendations for dealing with missing utility data for UK HTA.
METHODS: A simulated individual patient dataset, similar to those used to estimate utility values for a CUA submission for HTA in the UK, was developed. With this dataset, we simulated missingness at various levels and by different mechanisms. We assessed the performance of: complete case analysis (CCA), mean square estimation (MSE), linear mixed modelling (LMM) and multiple imputation via chained equations (MICE), and used this to make recommendations for handling and reporting of missing data in IPD that will be submitted to decision-makers in the UK.
RESULTS: Regardless of the mechanism or the level of missingness, MICE and LMM always resulted in substantially less error when calculating health state utility. Where data were missing at 30%, CCA and MSE were often associated with a substantially higher mean difference than MICE and LMM. The standard deviation was always substantially depressed regardless of mechanism or level of missingness when LMM was used.
CONCLUSIONS: It is our recommendation that an assessment of the mechanism and magnitude of missingness be made and reported for all data sets that are used to inform economic models for HTA in the UK as a minimum standard. Further, we recommend that CCA is almost never appropriate and our research, echoed by others supports the use of MICE as standard.
Lotte Klim
LEO Pharma, Copenhagen, Denmark
OBJECTIVES: Tenosynovial giant cell tumor (TGCT) is a locally aggressive neoplasm that involves the synovium, bursae, or tendon sheath. Tumor location varies and is often associated with joint destruction, pain, stiffness, and limited range of motion (ROM). The objective of the current analysis was to define a meaningful change in clinical outcome assessments (COAs) measuring active range of motion, physical function and stiffness among patients with TGCT.
METHODS: Qualitative embedded exit interviews were conducted with patients participating in the double-blind MOTION Phase 3 trial (NCT05059262) of vimseltinib vs placebo for TGCT. Patients were asked about meaningful changes in Patient Global Impression of Change (PGIC) and Patient Global Impression of Severity (PGIS) anchors, active ROM, physical function (Patient-Reported Outcomes Measurement Information System (PROMIS) – Physical Function (PF)), and stiffness (Worst Stiffness Numeric Rating Scale (NRS)). Subsequent quantitative anchor (using PGIS and PGIC) and distribution-based analyses of the clinical trial data, informed by the results of the qualitative interviews, were used to triangulate and define meaningful within-patient changes. Analyses were conducted on blinded data prior to trial primary analyses.
RESULTS: A total of 123 patients were randomized in MOTION with 96 (78%) completing an exit interview. The PGIC anchor variables had moderate correlations with changes in the COA measures (range: |0.38 to 0.54|). The data for both the PGIC-PF and PGIC-ROM support that most patients (PGIC-PF: 67%; PGIC-ROM 73%) considered “minimally improved” responses on these questions to represent clinically meaningful changes. Anchor- and distribution-based estimates for PROMIS-PF ranged from 1.45 to 4.9, for active ROM from 6.0 to 16.4, and Worst Stiffness from -2.3 to -0.5.
CONCLUSIONS: Combined evidence from anchor and distribution-based methods supported by patient insights from qualitative interviews informed the final selection of MCIDs of +3-points for PROMIS-PF, +10% for active ROM, and -2-points for Worst Stiffness NRS.
OBJECTIVES: As healthcare increasingly prioritizes patient-centered care, the use of patient-reported outcome measures (PROMs) to evaluate clinical practice has grown. A new area of research investigates whether PROM outcomes can inform consultations between patients and providers. However, limited evidence exists on how PROM data is presented to clinicians and patients. This study aims to understand current practices in PROM presentation in patient dashboards and explore the main challenges involved in designing these dashboards.
METHODS: We conducted semi-structured interviews with stakeholders involved in PROM dashboard design. The focus was on dashboards that communicate patient outcomes and are intended for use by clinicians and/or patients to inform disease management decisions.
RESULTS: Six dashboards were reviewed, and eight stakeholders were interviewed. Design decisions were predominantly derived from either the stakeholders' assumptions or specific testing with the users.
CONCLUSIONS: The study identifies key considerations in designing PROM dashboards. However, many current design decisions are based on assumptions about patients’ and clinicians’ needs, wants, and preferences which may not always align with actual user experiences. More research is needed to improve our understanding of how to present PROM data to aid user comprehension and facilitate decision-making.
OBJECTIVES: Goal attainment scaling (GAS) is a clinician-facilitated patient-centric clinical outcome assessment that quantifies the impact of an intervention on personalized goals. Although it has many strengths, such as being highly responsive, inherently clinically meaningful, and non-arbitrary, a lack of standardization in its implementation may challenge its reliability and validity. Here, we utilized a goal inventory to standardize GAS implementation and assessed the acceptability of this approach from the patient/caregiver’s perspective.
METHODS: Twenty-six patient/caregiver dyads (65+ years) participated in goal-setting interviews with four clinicians trained in GAS. Dyads were given a GAS orientation handout (with or without a goal inventory) and asked to set at least one goal. The goal inventory included 37 goals covering multiple domains. GAS acceptability was assessed using multiple-choice surveys (n=10, n=6) and open-ended questions (n=11, n=7) at baseline and follow-up. GAS acceptability was scored, with higher scores representing greater acceptability. Data were analyzed using Mann-Whitney tests, and open-ended responses were analyzed thematically.
RESULTS: Participants set a total of 71 goals [range=1-4]. There was no statistically significant difference between the number of goals set between participants who used an inventory (x̄=2.9) and those who did not (x̄=2.5; p=0.13) or between the acceptability of inventory-facilitated (1.15±0.25; 1.27±0.34) and standard GAS (0.96±0.24; 1.42±0.33) at baseline (p=0.12) or follow-up (p=0.35) respectively. Setting treatment goals resulted in a better-than-usual relationship with the clinician independent of goal inventory use. An enhanced understanding of the scope of treatment and increased perceptions of being cared for by the clinician were commonly noted, but only for inventory users.
CONCLUSIONS: Utilizing an inventory during goal setting to standardize the implementation of GAS is acceptable. However, future work is needed to assess its impact on the goal type, goal scale quality, and the time taken to develop GAS scales and facilitate its use across the phases of drug development.
OBJECTIVES: Existing clinical outcome measures of the core features of autism, social communication and interaction, as well as restricted and repetitive behaviours have shortcomings in assessing treatment effects. Goal attainment scaling (GAS) offers the potential to measure individual experiences, while Global Impression (GI) items provide a holistic assessment of treatment benefit. The objective of this study was to develop and cognitively debrief a novel GAS and GI items in paediatric autism.
METHODS: A comprehensive literature review was first completed to understand potential treatment benefits and goals in autism. This was followed by a qualitative study with two waves of interviews. The first ‘Concept Elicitation’ interview round (N=40) included autistic adolescents aged 12-17 years, parents of autistic children aged 5-17 years and clinicians, the purpose was to generate personalised goals and GI items. The second ‘Cognitive Debriefing’ interview round (N=39) was to obtain feedback on a draft GAS manual and GI items. An external expert panel provided input into all aspects of the study.
RESULTS: Twenty-five personalised goals were generated for core characteristics (communication, socialisation, restrictive and repetitive behaviours) and associated characteristics of autism for children and adolescents. Feedback from the cognitive debriefing supported the use of personalised goals. Adolescents/parents found these helpful, and clinicians recommended using these goals in clinical research. Nine GI items were generated for assessing impact/difficulty and change in symptoms; these can be rated by adolescents, parents of children with autism and clinicians.
CONCLUSIONS: As autism is a heterogeneous condition, use of personalised goal measures and tailored GI items may overcome some of the challenges with existing outcome measures. The FDA’s Patient-Focussed Drug Development Guidance-4 highlights the patient focused nature of outcomes like GAS in clinical trials. These findings will inform future measurement strategies for clinical studies in paediatric autism.
Eleanor Perfetto, PhD, RPh, MS
University of Maryland School of Pharmacy, Baltimore, MD, USA
Michelle Campbell, PhD
US Food and Drug Administration, Silver Spring, MD, USA
Gunnar Esiason, MBA, MPH
Boomer Esiason Foundation, New Canaan, CT, USA
Angela Rylands, PhD, CPsychol
Kyowa Kirin International plc, Marlow, BKM, United Kingdom
PURPOSE: To present the benefits and applications of integrating synthetic preference data into digital twins for enhancing personalized healthcare decision-making. Synthetic data may solve data scarcity, privacy concerns, and evidence transportability. Synthetic data generation methods promise to replicate real-world scenario complexity and representativeness. Researchers can simulate alternative treatment plans and explore resulting outcomes to optimize decision-making by enabling the creation of digital twins. We will explore the innovative use of synthetic data for real-time elicitation of patient preferences in a wide range of decision-making settings. The application of digital twins in the studies of patient behavior and statistical methods available to develop synthetic preference data will be presented.
DESCRIPTION: Workshop attendees will obtain a working knowledge of the fundamentals of digital twins and the application of synthetic preference data. The workshop will review a). the basics of synthetic data, b). methodological advancement needed to develop synthetic data for preference studies c). practical applications of synthetic preference data in the further development of personalized healthcare. Dr Mühlbacher will chair the session, introducing key definitions and contextualizing the applicability of synthetic data in the field of preference studies (10 min). Dr Manca will illustrate the causal inference underpinning the validity of the models that produce predictions to generate synthetic data and make generalizable statements (15 min). Dr Kolasa and Dr Zah will exemplify the benefits of incorporating synthetic patient preferences in personalized healthcare, enhancing medication adherence, and managing chronic diseases through personalized interventions (15 min). The highlight of the workshop is the audience participation, involving the identification of problems and solutions for a hypothetical case study in surgical planning and risk management (20 min). This interactive workshop will be valuable for researchers, clinicians, and industry analysts interested in integrating synthetic data with real-world data for a broader range of decision-making applications.
Axel Mühlbacher, PhD, MBA
Hochschule Neubrandenburg, Neubrandenburg, Germany
Katarzyna Kolasa, PhD
Óbuda University, Budapest, PE, Hungary
Andrea Manca, MSc, PhD
University of York, Heslington, York, North Yorkshire, United Kingdom
Oriana Ciani, PhD
SDA Bocconi School of Management, Milan, MI, Italy
Jeanette Kusel, MSci, MSc
National Institute for Health and Care Excellence, London, United Kingdom
Mario Ouwens, PhD
AstraZeneca, Mölndal, O, Sweden
Rob Abbott
Eberechukwu Onukwugha, BA, MSc, PhD
Brian O'Rourke, PhD
RCMS, Ottawa, ON, Canada
Laura Pizzi, PharmD, MPH
ISPOR, Lawrence Township, NJ, USA
Truveta
Ryan Ahern, MD
Truveta, Bellevue, WA, USA
Office of Health Economics
Conchi Biurrun, MA
Federación Española de Cáncer de Mama, Santander, Cantabria, Spain
Giuseppe Curigliano, MD
Division of New Drugs and Early Drug Development, European Institute of Oncology, IRCCS, Milan, Italy
In this forum, we will review the current status of the regulation’s implementation and provide a detailed explanation of the European regulation. Join us to explore the progress and challenges faced by individual Member States and understand how these regulations are shaping healthcare delivery across the EU.
Natividad Calvente, Pharmacy
Novartis Group Spain, Madrid, M, Spain
Phani Veeranki, MD, DrPH
Optum LifeSciences, CYPRESS, TX, USA
Shuvayu Sen, PhD
Merck & Co., Inc., Kenilworth, NJ, USA
Patrice Verpillat, MD, MPH, PhD
European Medicines Agency, Amsterdam, NH, Netherlands
The forum will begin by highlighting the importance of medication adherence in clinical trials, discussing the critical role it plays and the consequences of neglecting this issue. It will then provide an overview of the current regulations from the EMA and leading HTA bodies in the EU regarding approaches to considering medication adherence in clinical research. To offer practical guidance, the forum will introduce the ABC Taxonomy for Medication Adherence, which includes recommendations on defining medication adherence. Additionally, the Timelines-Events-Objectives-Sources (TEOS) Framework will be presented to guide the operationalization of medication adherence concepts in clinical research. The discussion will then shift to promoting transparent reporting methods, based on the International Society for Medication Adherence (ESPACOMP) Medication Adherence Reporting Guideline (EMERGE), to ensure clarity and accuracy in adherence data reporting.
The forum will utilize case studies to highlight common problems associated with medication non-adherence in clinical trials and provide examples of successful strategies to address these issues. Real-time polling will be used throughout the forum to facilitate interactive audience discussions on the challenges of medication adherence in clinical research. Participants will be encouraged to share their experiences and perspectives. The concluding segment of the forum will allow time for wrap-up and provide opportunities for questions and answers. This will enable attendees to clarify any doubts and reinforce their understanding of the key points discussed.
Mickaël Hiligsmann, PhD
Maastricht University, Maastricht, Limburg, Netherlands
Tamas Agh, MD, PhD
Syreon Research Institute, Budapest, PE, Hungary
Bijan Borah, PhD
Mayo Clinic College of Medicine and Science, Rochester, MN, USA
Lina Eliasson, PhD, CPsychol
Sprout Health Solutions, Pinner, LON, United Kingdom
In recent years, advances in artificial intelligence (AI), particularly with large language models (LLMs) such as the Generative Pre-trained Transformer 4 (GPT-4), have shown potential to revolutionize various domains by automating complex cognitive and reasoning tasks. The aim of this session is to discuss how LLMs can be utilised in HEOR reasoning problems, specifically related to conceptualising the natural history of the disease and recommending potential model structures.
The session will cover the following topics:
ConnectHEOR
Shilpi Swami, MSc
ConnectHEOR, London, United Kingdom
Robert Boer, PhD
AbbVie, Irvine, CA, USA
Tushar Srivastava, MSc
PT19 Emerging Trends in the Adoption of Alternative Financing Models for Advanced Therapy Medicinal Products: A Scoping Review
PT20 Impact of Huge-Seller Repricing on Price Trend for Reimbursable Cancer Drugs in Japan
PT21 Promoting Access to Innovative Drugs: A Review of National Drug Price Negotiation in China
PT22 Evaluating Reimbursement Pathway of Digital Health Technologies (DHT) Across Five European Countries
PT23 The Evolving German DiGA Market: Insights From a Claims Data Analysis
PT24 From French Early Access to Reimbursement: A Comparison of Early Access Prices and List Prices
PT13 Cost-Effectiveness Analysis of Stool-Based Colorectal Cancer Screening Tests for Individuals Aged 45 Years
PT14 Economic Evaluation of an eHealth Application Fostering Palliative Care for Cancer Patients in Greece
PT15 Utilization Trends and Access Disparities in CAR T-Cell Therapy: Real-World Insights From an All-Payer US Healthcare Database (2017-2024)
PT16 Economic Influences on Medical Decision-Making in Cancer Medicine: An Analysis of Qualitative and Quantitative Data
PT17 Leveraging External Priors to Address Population Variability in a Network Meta-Analysis: A Case Study in Advanced Esophageal Squamous Cell Carcinoma
PT18 Preferences for and Importance of Early Oncology Trial Endpoints to Canadians With Early-Stage Cancer: An Interim Analysis of a Qualitative Patient Preference Study
ISSUE: Global oncology spending is continuously rising. Simultaneously, drugs are increasingly approved based on less comprehensive evidence to ensure timely access, hampering assessments of their added benefit and often resulting in negative ratings. Concerns are growing regarding the financial strain on health systems and misalignment between incentives in the pharmaceutical market and patient interests, i.e. access to beneficial drugs for critical needs.
OVERVIEW: First, Francine Brinkhuis will present findings (~10 minutes) from a BMJ-published study*, co-authored by herself and Wim Goettsch, revealing that many EMA-approved oncology drugs (1995-2020) lack demonstrated added benefit, especially those approved via expedited pathways. These pathways aim to accelerate access to drugs for unmet medical needs but often lead to expensive drugs without substantial clinical benefits. Further, while revenues seemingly align with added benefit, most oncology drugs recover R&D costs within a few years. All this underscores the policy gap between drug approval/reimbursement and patient/societal benefit.
https://www.bmj.com/content/384/bmj.q511
Utrecht University, Utrecht, Utrecht, Netherlands
Bettina Ryll, MD, PhD
Melanoma Patient Network Europe, and Past Chair of the Patient Advocates Working Group, European Society for Medical Oncology, Uppsala, Uppsala, Sweden
Tina Taube, Master
EFPIA, Brussels, Belgium
Steffen Thirstrup, MD, PhD, CMO
European Medicines Agency, Amsterdam, Netherlands
ISSUE: As demands upon healthcare resources increase, informal caregiving has gained greater consideration in determining the impact of health conditions. However, not all informal caregiving is the same. Variation exists within and across health conditions. Three key areas of debate will be discussed. 1) conceptualizing informal caregiver HRQoL for use within economic models. 2) measurement of informal caregiver HRQoL. 3) valuation and incorporation of informal caregiver HRQoL in economic evaluations. This session will reflect upon lived experiences of being an informal caregiver of a person with a rare, progressive life-limiting condition, explore the differences of informal caregiving across the different groups, and consider how to account for these differences when selecting measures and creating economic models to ensure the correct values are captured, and reflect upon whether values attributed to informal caregiving should be the same across health conditions.
OVERVIEW: Fleur Chandler will first offer an overview of the lived experience and variability of being a caregiver (15 minutes), reflecting upon key domains of HRQoL affected. Jill Carlton will discuss the suitability of existing instruments to measure informal caregiver HRQoL, focusing upon content validity and acceptability. She will reflect upon whether societal views of informal caregiver HRQoL impact should differ according to issues such as rare conditions, and/or life-limiting conditions. Alice Biggane will discuss the need for wider conversation and collaboration between sponsors and payers to ensure alignment on how to capture caregiver HRQoL and the impact of potential treatments in health technology assessments and cost-effectiveness analyses, without leaving value behind (10 minutes). Alice Biggane, Jill Carlton and Fleur Chandler will be giving their thoughts on these areas from the different perspectives; industry, academia and from lived experience during a panel discussion lead by Sally Vincent. Sally will also facilitate questions from the audience (20 minutes).
Sally-Anne Vincent, BSc, MSc
Vitaccess, Reading, RDG, United Kingdom
Alice Biggane, MPH, PhD
Pfizer, Tadworth, SRY, United Kingdom
Jill Carlton, BMedSci, MMedSci, PhD
University of Sheffield, Sheffield, Yorkshire, United Kingdom
ISSUE: Stakeholders from across the healthcare and life science spectrum are watching as the European Parliament and the Member State representatives in the Council debate the legislative proposal of the Commission to substantially review the EU’s legislative framework for pharmaceuticals. The common goal is to ensure better and faster access, availability and affordability of new medicines in all EU Member States. But how well are the needs of patients and the member states’ healthcare systems addressed? Will the proposals and counter-proposals really meet their needs?
Petra Wilson, PhD
Virginie Bros-Facer, MSC PHD neuroscience
EURORDIS-Rare Diseases Europe, Paris, France
Johan Pontén, MA
Medicine Evaluation Committee (MEDEV), Stockholm, Sweden
Valentina Strammiello, MA, BA
European Patients’ Forum, Brussels, Brussels-Capital Region, Belgium
ISSUE: After decades of investment in the HEOR function, the biopharma industry appears to have entered a stage in which it is actively questioning what it has gotten in return. To date, little has been done to demonstrate the return on investment (ROI) of spending on HEOR personnel and research activities. This contrasts sharply with other components of biopharma spending, in which measurement of financial impact and calculation of ROI are routine. Do we need to demonstrate the ROI of HEOR? Can we?
OVERVIEW: Since individual health economists first started getting hired by industry in the 1980s, the HEOR function has grown to become a sizable component of the typical biopharma company org structure and annual budget. Core elements of HEOR—economic modeling, patient-reported outcomes, real-world evidence, and the like—have become fundamental for demonstrating product value to health system stakeholders. The beneficial impact of HEOR has largely been taken as a given rather than open to question, amid the common notion that HEOR activities and data are table stakes to successful launch and market uptake of biopharmaceutical products. This issue panel will consider the importance of demonstrating the ROI of HEOR and the difficulties in framing the calculation. Laura Pizzi, the panel moderator will outline key questions for the discussion, including: What is currently known about the ROI of HEOR? How can we formalize ROI calculations for HEOR work? Sissi Pham will provide an overview of current thinking within the biopharma industry on the role and value of the HEOR function. David Thompson will outline alternative approaches to ROI calculation and corresponding estimation challenges. Jens Grueger will provide a management consulting perspective on how HEOR and related functions can be championed within biopharma organizations. The moderator will solicit audience interaction and feedback on the alternative viewpoints.
Sissi V Pham, Pharm.D.
AESARA, Chapel Hill, NC, USA
David Thompson, PhD
Rubidoux Research LLC, Manchester, MA, USA
ISSUE: EU JCA aims at establishing a framework that promotes research, innovation, and more equitable access to products with large unmet medical needs. Process and timelines for submission and review have been recently finalized. While the 27 member states are still finalizing national processes and regulations, the fundamental question remains: is the designed process going to expedite patients’ access in EU?
OVERVIEW: provided by Cristina Masseria. Processes and timelines were designed to guarantee high quality and a voice for all member states, providing an opportunity for raising the EU perspective, maximizing synergies, and eliminating inequity across the EU. However, the maximum number of Population, Intervention, Comparison and Outcomes (PICOs) has not been set and will vary by therapeutic areas. The potential requirements of Indirect Treatment Comparison to meet all the PICO requirements may be substantial in terms of additional RCT statistical analyses and data gathering. The opportunity for the pharmaceutical company to engage with the EU is limited, with a very tight submission timeline. Companies are incentivized to initiate local reimbursement at the same time in all member states following EU JCA complete assessment, removing the possibility for accelerated submission. Moreover, there is risk of losing innovation funding for certain therapeutic areas with implications on pipeline selection.
Cristina Masseria, MSc, PhD
ISPOR, NEW YORK, NY, USA
Fabrizio Gianfrate, PhD, MSc
University of Ferrara, Ferrara, Italy
Francis Pang, MBA
Orchard Therapeutics Ltd, London, LON, United Kingdom
Gergana Zlateva, BA, MPA, PhD
Pfizer Inc., New York, NY, USA
Stephen Duffield, PhD, MD
National Institute for Health and Care Excellence, Liverpool, LAN, United Kingdom
Barbra Dickerman, PhD
Harvard T.H. Chan School of Public Health, Cambridge, MA, USA
Matthew Dixon, PharmD, PhD
Bristol Myers Squibb, Lawrenceville, NJ, USA
Grace Hsu, MSc
Cytel Inc., Waltham, MA, USA
ISSUE: Wide variations in clinical practice and the availability of treatments across the EU will likely result in many PICOs being identified when scoping Joint Clinical Assessments (JCAs). This has been confirmed in multiple PICO simulation exercises including the ones undertaken in the EFPIA and Evidera report. Whilst good-quality direct comparative evidence is optimal, the likely number of PICOs mean this will not fully address the scope of many JCAs underscoring the need to understand the experience and current use of state-of-the-art evidence synthesis methods, and their strengths and limitations to facilitate decision-making in the context of JCAs.
OVERVIEW: EFPIA and Evidera conducted a simulation to explore the impact of the proposed EUnetHTA21 methodologies on several widely reimbursed oncology treatments. The simulation of the scoping process resulted in many potential PICOs for each product, ranging from 16 up to 57 depending on the indication and line of treatment.
Context is key to the use of different evidence synthesis methodologies, the simulation demonstrated the central role indirect treatment comparisons, real-world evidence, single-arm studies, and oncology-relevant endpoints play in country appraisals to help address uncertainty. The simulation found that a wide range of evidence synthesis approaches will be essential to conduct comprehensive JCAs and underlined the importance of considering the totality of evidence or endpoints available at the time of submission. With the publication of methodological guidelines over the next few months, it will be important for assessors and technology developers to understand how best to use state-of-the-art methodologies in JCAs and apply learnings from these previous experiences.
The issue panel will start with a short presentation highlighting the issues identified followed by a structured panel discussion with recommendations and audience questions. This panel will be beneficial to stakeholders involved in evidence generation, HTA, and JCA processes.
Martin Parkinson, MRPharmS
Evidera, part of PPD clinical research business, Thermo Fisher Scientific, London, London, United Kingdom
Anna Chaimani, PhD
Inserm, Université Paris Cité, Paris, France
Niklas Hedberg, MSc
The Dental and Pharmaceutical Benefits Agency (TLV), Stockholm, AB, Sweden
James Ryan, MSc
AstraZeneca, Cambridge, CAM, United Kingdom
Sophie Van Tomme, PhD
Sanofi, Amsterdam, Netherlands
5:30PM - 5:45PM
OBJECTIVES: In Flanders, population screening programs for breast, cervical, and colorectal cancer are well-established. Lung cancer screening has not yet been implemented, despite its potential to significantly reduce lung cancer mortality through low dose computed tomography (LDCT) screening. Policymakers are evaluating whether a comprehensive lung cancer screening program could be introduced soon. To ensure successful implementation, it is crucial to understand the preferences and concerns of eligible participants. This study will identify key factors influencing screening participants' engagement, enabling healthcare professionals (HCPs) to address critical issues and enhance the screening program's overall effectiveness.
METHODS: An umbrella review and semi-structured interviews with eligible lung cancer screening participants (n=15) and HCPs (n=15) in Flanders will be held. Interviews will be transcribed ad verbatim and analyzed using thematic framework analysis. The themes identified from both the umbrella review and interviews will be categorized according to the CFR 2.0 framework [1]. The results will be narratively synthesized.
RESULTS: Preliminary findings from the umbrella review highlight key barriers to lung cancer screening. These include widespread concerns such as fear of diagnosis, concerns about radiation exposure, the financial burden associated with screening procedures, and logistical challenges such as transportation issues. Further insights and detailed results from the ongoing literature and planned interview study will be presented during the ISPOR 2024 congress.
CONCLUSIONS: To establish an effective lung cancer screening program in Flanders, addressing barriers such as fear of diagnosis, radiation exposure concerns, financial limitations, and logistical challenges is crucial. Considering these preferences and concerns can enhance the development of a more effective screening program in Flanders. In a next step, this approach facilitates tailoring screening recommendations to each person's individual needs during shared decision-making counseling visits.
5:15PM - 5:30PM
OBJECTIVES: Newer immunotherapies, such as chimeric antigen receptor T-cell therapy (CAR-T) and T-cell engaging bispecific antibodies (BsAbs), offer innovative ways to treat hematological cancers (i.e., myeloma, leukemia, and lymphoma). While these novel immunotherapies offer promising survival outcomes, they are not without potentially serious side effects and impacts on Quality of Life (QoL). There are currently no specific Patient Reported Outcome Measures (PROMs) for reflecting these treatments. This research aimed to understand the challenges around implementing PROMs while patients receive novel immunotherapies. We sought to identify ways to encourage alignment, consistency, and interpretability of data collected using PROMs for novel immunotherapies.
METHODS: In March 2024, we held two two-hour virtual workshops with participants representing the myeloma, leukemia, and lymphoma patient communities, regulators, payers/ HTA bodies, industry, academic researchers, clinicians, and trialists.
RESULTS: Twenty-nine representatives participated across the online workshops. Key takeaways include: 1) PROMs in use today do not capture all symptoms and side effects important to patients and other stakeholders; 2) current measures do not sufficiently capture patients’ short-and long-term novel immunotherapy treatment experiences; 3) while there is no consensus regarding timing and frequency of long-term follow-up, workshop participants thought a reasonable approach to measuring long-term effects could include administering PROMs every 6 or 12 months during long-term follow-up, as well as an annual assessment of role and cognitive function.
CONCLUSIONS: PROMs currently used in studies about novel immunotherapies do not address all patient-important outcomes or treatment-related side effects, which can severely impact patients’ QoL. Current QoL assessment approaches do not address the unique attributes of novel immunotherapies. Assessments should be tailored to understand treatment experiences and patient outcomes. Collaboration between patients, clinicians, researchers, and industry representatives is essential to develop and refine PRO tools and accurately reflect real-world experiences of patients undergoing treatment with novel immunotherapies.
5:45PM - 6:00PM
OBJECTIVES: Non-adherence to lipid-lowering agents presents significant risks to patients and compromises the effectiveness of treatment. This study aims to qualitatively identify the barriers to lipid-lowering medication adherence, understand the considerations of patients with hypercholesterolemia when selecting these agents, and guide the design of a medication preference study.
METHODS: Focus group interviews were conducted with Cantonese-speaking patients diagnosed with hypercholesterolemia in Hong Kong. These participants were recruited via convenience sampling by cardiologists at a university-affiliated hospital. The interviews were structured into three parts: exploring patients' perceptions of their condition and treatment, identifying crucial factors that influence their selection of lipid-lowering agents, and engaging in medication preference tasks designed using the Discrete Choice Experiment (DCE) method. Thematic analysis was applied to organize the codes from the transcripts into broader themes.
RESULTS: Twenty patients took part in the focus group interviews conducted on the university campus between January and March 2023. Four primary themes emerged: issues in medication management, patients' medication preferences, and the structure and understanding of preference tasks. Barriers to adherence included insufficient knowledge, high pill burden, poor communication with healthcare providers, minimal involvement in treatment decisions, limited access to information about medications, side effects, and forgetfulness. Factors influencing medication choice included the treatment regimen, effectiveness, side effects, doctors' recommendations, drug interactions, and out-of-pocket costs. Despite suggestions for altering attributes and levels, the medication preference tasks effectively captured patients' trade-offs.
CONCLUSIONS: Understanding the barriers to medication adherence and the factors that influence medication choice underscores the importance of considering patients' viewpoints. These findings can guide decision-makers in choosing medications that align with patient preferences, ultimately fostering better adherence.
5:00PM - 5:15PM
OBJECTIVES: This qualitative interview study described overall preferences of people living with HIV-1 and healthcare providers (HCPs) regarding attributes associated with HIV-1 treatment, particularly long-acting injectable options, and factors influencing these preferences.
METHODS: Individual qualitative interviews with people living with HIV-1 and HCPs in the United States were conducted to assess overall preferences when considering a treatment switch for the following attributes related to HIV-1 treatment previously identified from a targeted literature review: mode of administration, treatment administration setting, treatment administration frequency, injection location on body, and side effects. Influencing factors and spontaneously mentioned attributes were also recorded.
RESULTS: Interviews were conducted with 50 people living with HIV-1 and 5 HCPs (physicians, n=3; nurse practitioner, n=1; physician assistant, n=1). The majority of people living with HIV-1 were male (60%), with no prior injectable experience (62%), and had a mean age of 48.6 (range: 23-70) years. Both groups ranked side effects as highest priority, followed by treatment administration frequency. Highest-ranked spontaneously mentioned attributes among people living with HIV-1 were cost and number of injections. Regarding side effects, people living with HIV-1 were most concerned about weight gain, diabetes, and adverse gastrointestinal effects. Most people living with HIV-1 preferred 4- and/or 6-month intervals for HCP-administered injections (96% and 95%, respectively), citing alignment with appointments and HCP relationships, and 3-month intervals for self-administered injections (94%), considering storage and disposal. Oral administration was preferred by 92% of people living with HIV-1.
CONCLUSIONS: People living with HIV-1 prioritize minimizing side effects and reducing injection frequency when making treatment decisions. A planned quantitative discrete choice experiment will assess generalizability of these findings in a larger sample with broader geographical reach and provide evidence of risk tolerance and perspectives on benefit regarding different HIV-1 treatment modalities for health technology assessment and regulatory bodies.
Maciej Niewada, PhD
Medical University of Warsaw, Warszawa, Poland
OBJECTIVES: Survival analyses are a core part of many HTA submissions where extrapolation of time-to-event clinical endpoints is required. The purpose of this research was to explore automation of survival analysis reporting using Generative Artificial Intelligence (GenAI). Following published best practices for curve selection, GenAI was leveraged to recommend an appropriate extrapolation curve and provide justifications.
METHODS: Data were taken from a previously accepted HTA survival analysis report (NICE TA817) for patients treated for resectable urothelial cancer (PD-L1 ≥1%), with a minimum follow-up of 11-months. GPT-4o was provided with survival analysis outputs, including statistical tests, survival probability estimates, and figures, to assess proportional hazards (PH) and goodness-of-fit. Prompted with relevant content, GPT-4o was asked to; 1) assess PH, 2) select suitable extrapolation models (dependent vs. independent), 3) consider external data, then 4) select an appropriate curve. To validate accuracy, GPT-4o’s results were compared with results in the original report, the report published by NICE, and assessed against the opinion of three expert health economists.
RESULTS: GPT-4o’s interpretation of log-cumulative hazard plots, Schoenfeld residual plots, and Grambsch-Therneau test results aligned with interpretations made by the three health economic experts, the human produced report, and the NICE committee. GPT-4o concluded that the PH assumption might be violated, therefore suggesting consideration of both dependent and independent parametric models. Based on a comprehensive analysis of goodness-of-fit, visual fit, and long-term external survival data, GPT-4o recommended the same survival curves as those selected in the original report and by the NICE Committee. Notably, 13/13 statements or decisions made by GPT-4o were consistent with the original report or expert opinion.
CONCLUSIONS: The results suggest automation of curve selection and reporting of survival analyses is possible. However, more research is required to determine generalizability with differing levels of data maturity and to test the performance of alternative GenAI models.
OBJECTIVES: Unmeasured confounding presents a key challenge when performing treatment comparisons using real world data. Quantitative bias analysis (QBA) is an important tool for assessing the robustness of treatment effect estimates obtained from such studies, but no published method is applicable to time-to-event (TTE) outcomes when the proportional hazards (PH) assumption is violated – a common circumstance when therapies with different molecular mechanisms are compared. This study developed, and assessed (with a simulation study), a method for QBA which used the accelerated failure time (AFT) model in order to relax the PH assumption.
METHODS: A two-step method was developed in which, firstly, the expectation-maximisation (EM) algorithm was used to impute an unmeasured binary confounder, U, with a known association with treatment assignment and the outcome. An AFT model was used for the TTE outcome, and a probit model for U. In the second step, an AFT model was fitted to the imputed data and a treatment effect obtained which was consequently adjusted for U. The method was assessed for accuracy using simulated data from a Weibull distribution over two data generating mechanisms which violated the PH assumption: delayed and waning treatment effect.
RESULTS: When using this two-step approach bias was small, particularly for the delayed treatment effect. Relative bias was 0.01% for delayed treatment effect and -0.10% for waning. Coverage achieved the expected level of 95% for both scenarios.
CONCLUSIONS: This study demonstrates a valid QBA framework which can be applied to TTE outcomes, without assuming PH. The framework can be readily applied, given the widespread use of both AFT models and the EM algorithm in statistical software; as well as being straightforward to interpret. This method could be applied to, for example, external control arm studies to formally demonstrate the robustness of results to unmeasured confounding.
OBJECTIVES: Using real-world data and evidence (RWD/RWE) in healthcare decision-making is increasing. However, acceptance of RWE varies among HTA agencies. Some agencies are more willing to consider RWE for evaluating new technologies and making decisions about healthcare resource allocation efficiently. We aim to explore the payer perspective on the barriers and opportunities of incorporating RWE as evidence submission in reimbursement decision-making by HTA bodies.
METHODS: We fielded an online survey with payers from the Ipsos payer panel across multiple EU and non-EU member states. Questions concerning the importance, acceptance, and aspects of RWE studies that are of concern for payers in HTAs were asked in this survey.
RESULTS: Payers indicate that national guidelines guide RWE use in HTAs in country where available (Canada, UK), while international, mainly EMA, guidelines are followed in countries such as Spain and Denmark. Major barriers to RWE adoption include data quality, methodology, and expertise. Transparency and data sources are crucial for payers, with pragmatic trials and registries being the preferred source to support recommendation decisions. RWE is considered valuable for indirect comparisons and treatments needing long-term management, in rapidly evolving landscapes, and with curative benefits after prolonged treatment. Some payers may accept to make HTA recommendations based only on RWE in some situation, such as rare diseases and gene therapies, when conducted according to guidelines.
CONCLUSIONS: The integration of RWE into HTA and access programs is advancing through initiatives aimed at improving data quality, methodology and relevance. Despite these efforts, RWE remains primarily supplementary to RCTs, except in specific cases. The ongoing focus on enhancing RWE’s credibility and use suggests a growing, although cautious, acceptance. Continued multi-stakeholders collaboration and development of standard frameworks to use the appropriate types of RWE and data sources in HTAs is a crucial step for making informed decisions.
OBJECTIVES: Causal inference methods allow for comparative analyses using real world data (RWD) when trials are unfeasible. Propensity scores (PS) are routinely used in RWD applications for tasks such as mitigating confounding, evaluating treatment effectiveness, and constructing external comparator arms in the absence of a suitable control group. Machine learning (ML) has the potential to improve these analyses by handling complex relationships in large datasets. This review examined ML methods for PS estimation in causal inference analyses.
METHODS: We performed secondary research to identify (i) simulation papers comparing non-parametric ML with logistic regression for PS estimation, (ii) theoretical papers discussing the application of ML in doubly robust estimators for both PS and outcome model estimation, and (iii) applied papers that use ML for PS estimation in real-world comparative effectiveness research.
RESULTS: Out of 9 applied papers analyzed, 5 used the super learner, 2 used neural networks and 2 used random forests for PS estimation. However, 5 simulation studies showed mixed results when comparing logistic regression or nonparametric ML models for PS estimation in terms of estimated effect bias, covariate balance and PS overlap. Three theoretical papers discussed potential bias in treatment effect estimation from using non-parametric ML for PS estimation due to regularization bias and overfitting risk. These issues can be mitigated in doubly robust methods with cross-fitting, leveraging non-parametric ML for both PS and outcome models, providing estimators robust against model misspecification.
CONCLUSIONS: Under certain conditions, ML can improve the validity of PS in estimating treatment effects from RWD. However, the importance of the “human factor”, such as variable selection based on expert knowledge, is crucial. Doubly robust methods, particularly when used with cross-fitting, can produce less biased treatment effect estimates. Future research should focus on the less-explored topic of using ML for PS estimation in the context of external comparator arm studies.
PURPOSE: This workshop aims to discuss best practices and challenges in expert elicitation for long-term survival estimates. We will present a comprehensive, step-by-step elicitation framework, providing attendees with essential practical knowledge of this method, its benefits, and implementation considerations. Perspectives from industry and health technology assessment (HTA) agencies will also be discussed.
Jessica Forsyth, PhD
University of Sheffield, Sheffield, Great Britain
Jacoline Bouvy, PhD
Min-Hua Jen, PhD
Eli Lilly and Company, Uxbridge, England, United Kingdom
Kate Ren, PhD
University of Sheffield|ConnectHEOR, Sheffield|London, England, United Kingdom
Richard Willke, PhD
Scintegral Health Economics, Chattangooga, TN, USA
Jason Shafrin, PhD
Lotte Steuten, PhD
Anne Willemsen, MSc
Dutch National Healthcare Institute, Diemen, Netherlands
Roisin Adams, MPharm, MSc., PhD
National Centre for Pharmacoeconomics, Dublin, Ireland & Discipline of Pharmacology and Therapeutics, Trinity College Dublin, Dublin, Ireland
Vanessa Schaub, PhD
F. Hoffmann-La Roche Ltd, Basel, Switzerland
Michal Stanak, Dr.phil, AKC
National Institute for Value and Technologies in Healthcare (NIH), Bratislava, Slovakia
Ian Bradley, BSc
P31 Academy for Consulting Performance Excellence, Peterborough, United Kingdom
Riku Ota, MPH
Novo Nordisk, Copenhagen, Denmark
Yan Zhi Tan, MSc
Lumanity, Utrecht, Netherlands
Solange Corriol-Rohou, MD, PhD
Pharmaceutical Company: AstraZeneca, GMD, Paris, 75, France
Pall Jonsson, BSc, PhD
National Institute for Health and Care Excellence (NICE), Manchester, LAN, United Kingdom
European Medicines Agency (EMA), Amsterdam, Netherlands
ISSUE: The European Union (EU) Health Technology Assessment Regulation (HTAR) will be applied from January 2025. From that date, all advanced therapy medicinal products and medical devices will have their relative clinical efficacy evaluated through a new EU Joint Clinical Assessment (JCA) pathway. Indirect treatment comparisons (ITCs) are expected to be frequently required to address the numerous PICOs (Population, Intervention, Comparator, Outcomes) requested in the JCA scope.
Sophia van Beekhuizen, MSc, MPhil
Cytel, Rotterdam, Netherlands
Mahmoud Hashim, PhD, MD
Johnson & Johnson, Rotterdam, Rotterdam, Netherlands
Iñaki Gutiérrez Ibarluzea, MSc, PhD
Fundación Vasca de Innovación e Investigación Sanitarias (BIOEF), Bilbao, Bizkaia, Spain
Amanda Cole, PhD
Claudio Jommi, MSc
Università del Piemonte Orientale, Novara, Italy
Julien Patris, MA
argenx, Brussels, Belgium
Center for Health Decision Science and Dept. of Epidemiology and Health Policy & Management, Harvard T.H. Chan School of Public Health, Institute for Technology Assessment and Department of Radiology; Massachusetts General Hospital; Harvard Medical School, Boston, USA
Beate Jahn, Assoc.Prof. Dipl.Math. oec. Dr.rer.soc.oec
Institute of Public Health, Medical Decision Making and Health Technology Assessment; Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL – University for Health Sciences and Technology, Hall in Tyrol, Tyrol, Austria
Patrícia C. Soarez, MPH, PhD
University of Sao Paulo, Sao Paulo, Sao Paulo, Brazil
Michael Urban, Dr. rer. nat. MBA, MSc
MED-EL Hearing Technology GmbH, Innsbruck, Tyrol, Austria
Praveen Thokala, MASc, PhD
The University of Sheffield, Sheffield, United Kingdom
OBJECTIVES: Obesity is a complex disease that can reduce patient quality-of-life and contribute to increased risk of complications such as type-2 diabetes (T2D), cardiovascular (CV) disease, and cancer, which are associated with economic and societal impact. To fully capture its many consequences, cost-effectiveness models for obesity mirror this complexity. The aim of this study is to compare the outcomes of two different modelling approaches for assessing the cost-effectiveness of tirzepatide in managing obesity.
METHODS: Two individual-level simulation models previously developed to assess cost-effectiveness of tirzepatide against diet-and-exercise (D&E) were compared using a United Kingdom population with obesity and overweight: a state-transition model and a discrete-event simulation model. Both models track individual metabolic factors such as body weight, high-density lipoprotein, and systolic blood pressure, to estimate how many patients in a simulated cohort would develop obesity-related complications over their lifetimes. Using the same risk equations and patient characteristics, models were restricted to consider common complications: T2DM onset, CV events, and onset of sleep apnoea. This allows evaluation of the impact of modelling approach on predicted outcomes.
RESULTS: Both models showed tirzepatide reduced the number of T2D and CV events such as stroke and myocardial infarction compared to D&E. However, the discrete-event simulation model estimated higher incidences of T2D and sleep apnoea across all treatment arms compared to the state-transition model. Time-to-event outcomes were estimated in the discrete-event simulation model only, demonstrating that tirzepatide is associated with delays in the onset of complications. While the state-transition model partitioned the CV events once, the discrete-event simulation model used a two-step partitioning approach. Similarly, the approaches in estimating non-CV deaths also differed between the two models.
CONCLUSIONS: Obesity is a complex disease associated with numerous complications. Despite the differences in approaches, both models provided directionally consistent results, validating their use in obesity cost-effectiveness studies.
OBJECTIVES: Many health technology assessment (HTA) agencies provide guidance on evidence standards for the use of surrogate outcomes. However, the level of detail varies, and there is limited guidance on considerations for economic models. With the increasing use of novel surrogate outcomes in clinical trials and consequently in economic models for HTA submissions, there is a need for further clarity around what to consider when utilizing these outcomes in economic models to inform decision-making.
METHODS: We conducted three scoping reviews; a review of regulatory standards for the validation of surrogate outcomes, a review of guidance from HTA agencies for the use and validation of surrogate outcomes and a review of statistical methods that have been proposed to validate surrogate outcomes. These reviews were supplemented with a qualitative study exploring how surrogate outcomes are dealt with in HTA evaluations by different HTA agencies and any lessons learned. The findings of these four strands of work were evaluated by the working group to inform the recommendations.
RESULTS: We developed recommendations outlining what should be considered alongside existing economic modelling guidance in HTA, when surrogate outcomes are used. This included considerations around their definition, justification, adoption, statistical validation, incorporation, reporting and approaches to quantify and present the uncertainty surrounding estimates based on their use.
CONCLUSIONS: The use of surrogate outcomes can pose a challenge for HTA agencies, because of the additional uncertainty in the relationship with longer term outcomes. Our recommendations can support economic modelers and decision makers especially for interventions in conditions where there are no long-term data available and when novel surrogate outcomes need to be used.
OBJECTIVES: Complex Excel-based cost-effectiveness models in health technology assessment (HTA) suffer from prohibitive computational run-time to perform probabilistic sensitivity analysis (PSA). Previous work aimed to shift Excel users to the R programming language through tutorials, example models, and R tools that automate PSA. Our objective is to circumvent this process with the REEEVR tool that can convert Excel models into R automatically.
METHODS: Using concepts from programming language interpreters we parse, tokenise, generate abstract syntax trees, and translate each cell of complex Excel-based models into R. This output is then ordered, streamlined, and combined with libraries. The process is wrapped into a graphical user interface.
RESULTS: The output of the converted R model exactly matched those of the Excel-based model for all implants; in both models, the NMB for the best implant was £209,790 (95% credible interval £201,143 – 205,310). The run-time was reduced from approximately 20.5 seconds in Excel to 0.26 seconds in converted R, an 80 times reduction. This was also substantially lower than approximate 1.37 second run-time of the hand-coded R model.
CONCLUSIONS: Our REEEVR tool can exactly convert a probabilistic Markov model from Excel into R with a greatly reduced run-time. The converted R model can also be more efficient than hand-coded R models. The example was informed by academia, industry and the NICE GDG, suggesting the REEEVR tool could have wide applicability in HTA.
OBJECTIVES: Despite its potential, the use of Generative AI in health economic modeling is still emerging. Leveraging Generative AI to streamline health economic model development could significantly benefit stakeholders by reducing the time and expertise required. Our objective was to evaluate the feasibility and accuracy of using Generative AI to develop a de novo health economic model.
METHODS: We used hepatitis C treatment as a case study for developing a de novo health economic model. Python was employed to interact with GPT-4 to develop a model structure for the natural history of hepatitis C by searching for published health economic models on PubMed. For parameter estimation, we utilized a multi-agent pipeline and OpenAI libraries. These parameters were then transferred to the R programming language's Heemod package to construct and run the Markov model. All of these steps were packaged into a customized solution, ValueGen.AI. The platform generated health states, transition probabilities, state and treatment costs, discount rates, cycle lengths, and time horizons. We compared the Generative AI-based model's structure, input parameters, and generated outcomes against expert opinion and published studies.
RESULTS: The Generative AI platform constructed a Markov-based model structure for hepatitis C with 10-15 health states and over 22 transitions between states. The platform also provided selected published models used as references. Additionally, the platform estimated values for transition probabilities, costs, and utilities, referencing publications for selected parameters. We repeated the experiment five times and observed variability in the model structure, attributed to variability in the referenced published models. Overall, the generated models had face validity.
CONCLUSIONS: We demonstrate the feasibility of developing a de novo Markov model for a chronic disease using Generative AI. Future research should focus on reducing variability in the model development process and comparing AI-generated model outputs with published models.
OBJECTIVES: Heart failure (HF) significantly impacts global healthcare. Remote monitoring, which allows for monitoring of patients in the home setting, has shown potential in improving HF management and reducing costs. This study examines factors influencing the willingness-to-pay (WTP) for telemonitoring among HF patients in The Netherlands, with focus on perceived usefulness, ease of use, social influence, and trust as predictors of perceived value and WTP.
METHODS: A survey was conducted in a group of 70 HF patients being monitored from a peripheral hospital. The survey explored how perceived usefulness, ease of use, social influence, and trust affected perceived value and WTP, based on the Theory of Planned Behavior (TPB) and Unified Theory of Acceptance and Use of Technology (UTAUT).
RESULTS: Perceived usefulness, ease of use, social influence, and trust did not significantly predict WTP. However, the analysis revealed a very strong positive significant correlation (0.97) between perceived value and WTP. Despite high perceived value and satisfaction with remote monitoring, patients generally exhibited low WTP. This discrepancy suggests that the Dutch healthcare system focus on universal access and minimal financial burden influences patient expectations, resulting in low WTP for remote monitoring. A regression analysis showed that the regression of the mediator perceived value on the dependent variable WTP, while controlling for overall trust, was significant (p = <0.001).
CONCLUSIONS: These findings highlight the importance of considering perceived value and the healthcare system context when evaluating patient WTP for remote monitoring. Further research should focus on trust and ease of use related to telemonitoring and alternative patient outcomes based payment models This knowledge is crucial for promoting adoption and sustainable financing of telemonitoring services to meet patient needs and ensure system sustainability.
OBJECTIVES: The purpose of the study was to explore the informational needs of patients, carers, and partners of people living with vasculitis in Australia.
METHODS: The questionnaire was developed by adaptation of the Vasculitis Specific Informational Needs Questionnaire (VINQ) and the Vasculitis Patient-Powered Research Network (VPPRN). A link to the Australian Vasculitis Patients Informational Needs Questionnaire (AVPINQ) was posted on the websites of Australian and New Zealand Vasculitis Society (ANZVASC) and LinkedIn.com. Patients responded to questions about their disease history, experience of patient education and source, and identified whether they wanted more information in any of the following areas: disease information, investigative tests, treatment options, physical support, psychological support, social support, employment support and financial support.
RESULTS: Seventy-eight questionnaires were completed and available for analysis. More than 90% of respondents (n=72) had not heard of their type of vasculitis before they received their diagnosis. 51% of respondents received information about vasculitis at the time of diagnosis. All respondents went on to source their own information following diagnosis. Despite accessing this additional information, 74.4% of respondents wanted information related to psychological state, 69.2% of respondents wanted more disease information, and 61.5% wanted to know more about their physical health. The order of preference of source of information was reported as verbal information from a doctor, written material alone, written material and a verbal conversation together, internet search, video, an educational group, verbal conversion with a nurse or clinical educator, and a 1–2-day course.
CONCLUSIONS: This study shows that Australian patients, carers and partners need information about vasculitis and the implications of living with this rare chronic condition. Patient education is an important aspect of disease management and patient-centered care. Developing specific vasculitis patient educational resources like those associated with more common chronic illnesses would benefit this patient group.
OBJECTIVES: As initiatives progress in the development of gene therapy for inheritable arrhythmogenic cardiomyopathies, this study, part of the GEREMY (Gene Therapy for treatment of rare inherited Arrhythmogenic Cardiomyopathy) project, is designed to gather insights into the unmet health-related needs of carriers. Needs range from impact on physical health and quality of care to impact on work and beyond. Results aim to tailor (gene therapy) drug development, healthcare settings and treatment decision-making to the needs of these patients.
METHODS: A literature review with a systematic search is being conducted in Pubmed and Embase, using “Arrhythmogenic Cardiomyopathy” and its synonyms to identify existing needs of these patients. Subsequently, a generic survey developed within the NEED (Needs Examination, Evaluation, and Dissemination) assessment framework is being disseminated among individuals diagnosed with Arrhythmogenic Cardiomyopathy at UZ Leuven (Belgium) and UMC Utrecht (The Netherlands). The survey is available in Dutch, French and English and includes 71 questions focussing on health-, healthcare- and social needs of patients. Descriptive statistics will be used to describe characteristics of participants. Several statistical tests will be performed on a selection of variables in order to identify potential relationships between demographic characteristics, clinical factors and the EQ-5D-5L utility scores.
RESULTS: The study has been initiated and data collection is currently ongoing. Full results will be presented on the conference.
CONCLUSIONS: This research forms the first phase of the UR-HEART (Understanding the preferences and unmet health-related needs of patients with arrhythmogenic cardiomyopathy) study, aiming to identify disease- and treatment characteristics that determine the needs of carriers and their willingness to make trade-offs between potential side-effects and anticipated benefits of current management and a future gene therapy.
OBJECTIVES: The National Health Council’s, Patient Experience Mapping Toolbox (PEMT) assists health economics and outcomes researchers (HEOR) in the acquisition of patient experience data (PED) to understand patients’ experiences in the health care ecosystem from before they receive a diagnosis to living with their diagnosis. Generating an informed decision and determining which treatment options are best for chronic disease patients is multifaceted. To better evaluate treatment options in the context of patients’ priorities, goals, and unique circumstances, we asked patients what factors are important when deciding among treatment options.
METHODS: From June 2022-August 2022, twenty-five key informant interviews were conducted with patients across various chronic disease areas (i.e., depression, atrial fibrillation, rheumatoid arthritis, asthma, etc.). Each interview was one hour and conducted via Zoom. This phenomenological study employed an inductive qualitative analysis to ascertain the perspectives of patients. Themes were derived on two topics: 1) treatment tradeoffs and 2) what questions about treatments and tradeoffs are most important to ask a health care provider.
RESULTS: For Treatments and Tradeoffs, three themes of importance were identified: 1) Insurance coverage and out-of-pocket costs, 2) Side effects and risks, and 3) Treatment efficacy and effectiveness. As for the type of questions that other patients should be asking health care providers, four themes arose: 1) Coverage and financial coordination of treatments, 2) Comparison and options of treatments, 3) Treatment/Knowledge about root causes of the disease, and 4) Alternative non-pharmacological treatments.
CONCLUSIONS: Cost, insurance coverage, and side effects are of high importance to patients when considering different treatment options. When patients were asked what they would tell other newly diagnosed patients to ask providers, these themes continued with additional perspectives on determining what factors caused the condition. PED derived through patient experience mapping is a way for HEOR to incorporate critical patient views and questions in models.
Danny Yeh, PhD
AESARA, Burlingame, CA, USA
National Taiwan University, Taipei, Taiwan
David Shum, PharmD, MBA, MSc
Hoffmann-La Roche Canada, Mississauga, ON, Canada
Anouchka Cecilia Vidal, PharmD, MBA
F. Hoffmann-La Roche LTD, Basel, Switzerland
Health Technology Assessment International, Manchester, Manchester, United Kingdom
Elizabeth Kopits, PhD
US Environmental Protection Agency, Washington, DC, USA
PURPOSE: To educate health economics and outcomes research (HEOR) professionals on the Patient Experience Mapping Toolbox (PEMT), a set of publicly available resources to help researchers engage and document patients’ experiences, its real-world applications, and how it complements other patient initiatives.
DESCRIPTION: Patient experience data (PED) and patient engagement are critical to patient-centered outcomes in healthcare delivery research, drug development, and regulation. Meaningful patient engagement is bi-directional, and patients must be included as equal partners from the inception. Developed by patients, the PEMT can be used to collect disease-specific PED to inform a range of uses, including drug development and joint clinical assessments. To date, the PEMT has been applied to studies that ascertain patients’ treatment tradeoffs, as well as the experiences of diverse patients with food allergies and chronic kidney disease in the U.S. The use of this tool could be beneficial to broader global audiences. Through this workshop, HEOR-EU professionals will hear from patient engagement experts demonstrating applications of the PEMT and identify opportunities to enhance their patient-centered research methods.
Silke Schoch, MA
National Health Council, Washington, DC, USA
Derick Mitchell, PhD
IPPOSI, Dublin, Ireland
Elisabeth Oehrlein, PhD, MS
Applied Patient Experience, LLC, Washington, DC, USA
Delta Hat Ltd, Nottingham, UK; University of Sheffield, Sheffield, Great Britain
Blythe Adamson, PhD, MPH
Flatiron Health, New York, NY, USA
Adam Brooke, MSc
The National Institute for Health and Care Excellence (NICE), London, United Kingdom
Jen-Yu Amy Chang, PhD, MSc
University of Sheffield, Sheffield, YOR, Great Britain
As both RWE and HEOR functions are dramatically evolving, we host a unique session that will allow you to develop concrete ideas about your own professional career. Leverage our experience and participate in the conversation, whether you are an expert or a leader, just starting your career or planning a next reasonable move. In this session we will review practical strategies which successful leaders and well-known experts use to grow and shape their profiles; we will share key success factors for career-development and address your questions and comments.
Herbert Altmann, PhD
Cencora, Munich, Germany
Natalia Scherff
Coach Scherff, Frankfurt, Germany
PT31 Reducing Health Inequality of Introducing SGLT2i Therapy in a Real-World Type 2 Diabetes Population With Diverse Patient Characteristics: A Distributional Cost-Effectiveness Analysis
PT32 Economic Evaluation of Abdominal Aortic Aneurysm Screening in the Czech Republic
PT33 Cost-Effectiveness of Adjuvant Radiation Therapy Versus Tamoxifen Alone in Older Adult Women With Early-Stage Breast Cancer
PT34 Evaluating the Healthcare Burden and Clinical Impact of Polypharmacy Among the Elderly in South Korea
PT35 Are Social Impact Bonds the Answer to Limited Healthcare Resources for Improving Health Promotion and Disease Prevention?
PT36 Assessing the Impact of Vision Impairment on Health-Related Quality of Life Among Community-Dwelling Older Population in Hong Kong: Preliminary Results
PT25 Potential for Standardization in Economic Assessment Across HTA Bodies: A Case Study in First-Line Treatments of Non-Small Cell Lung Cancer
PT26 From Clinical Assessment to Drug Reimbursement in Spain: Current Timelines and Potential Impact From the Joint Clinical Assessment
PT27 Time to Reimbursement in Ireland: Utilizing Regression Analysis to Assess Factors Contributing to Overall Timelines
PT28 Do We Say “Game Over” When OS Data Is Not Mature at Launch? A Review of Value Drivers for Oncology Products in the Absence of Significant OS Gains
PT29 From European Union Joint Clinical Assessments to Local Health Technology Assessments: An Environmental Scan of Methodological Guidance Across Key European Markets
PT30 Navigating the New European Member State Coordination Group on Health Technology Assessment Quantitative Evidence Synthesis Guidelines
Economic models play a crucial role in demonstrating the value of a new product at launch in HTA markets. To accurately illustrate incremental value of a new product relative to the standard of care (i.e., comparator), and evaluate its cost effectiveness in a specific population, health economists must navigate through many input parameters to implement in the economic model, as well as conceptualize the model framework, optimal for the product at hand.
This HEOR Theater presentation will explore the benefits of an integrated, cross-functional approach that incorporates payer insights upfront to inform and validate economic model framework and inputs from various sources which include clinical trials and real-world evidence. The session will demonstrate how this approach of planning for payer insights at various stages of economic model development enhances the likelihood of success and drives impact in reimbursement decisions. Speakers from the economic modeling consultancy and life sciences industries will share examples illustrating this concept.
By uniting economic modeling methodology and practical payer perspectives, the presenters aim to provide a comprehensive framework for developing robust economic models that stand up to scrutiny in the access and reimbursement environment. This approach will assist life sciences companies to avert the scenarios of heavy criticism and illustrate how insight-based economic model development may have helped.
Genesis Research Group
Dominic Jones-Phillips, PhD
Smita Kothari, PhD, MBA
Merck & Co. Inc., Rahway, NJ, USA
This interactive forum will provide attendees with invaluable insights into best practices and emerging developments in SEE, specifically tailored for HTA submissions. Min-Hua Jen will open the session by providing a concise overview of SEE and highlighting the necessity of high-quality SEE for HTA submissions from an industry perspective. Salah Ghabri will present an update on the ISPOR Good Practice Report, outlining key recommendations for the use of different SEE protocols in healthcare decision making. Kate Ren will introduce new developments in SEE specifically designed for long-term survival outcomes, focusing on innovative methodologies that ensure coherent estimates of long-term survival with meaningful expert rationale, thereby addressing a critical component of economic modelling in HTA. Dawn Lee will share her practical experiences of conducting SEE, providing real-world insights and lessons learned.
The session will conclude with an interactive Q&A segment, allowing attendees to engage directly with the experts, and discuss specific challenges and considerations in the application of SEE, such as estimating conditional outcomes, defining target populations for estimation, handling the impact of subsequent treatments on overall survival, and integration into subsequent modeling efforts.
Salah Ghabri, PhD, HDR
Haute Autorité de Santé (HAS), Paris, 75, France
PenTAG, University of Exeter, Exeter, DEV, United Kingdom
School of Health and Related Research, University of Sheffield; ConnectHEOR, Sheffield, NYK, Great Britain
Laura Housman will moderate the panel and provide a 5 outline of the issue including the difference between direct medical costs, non-medical costs, and indirect costs. Taylor Schwartz will provide a 10 minute overview of recently completed studies that utilized novel methodologies to measure non-medical costs and the broad indirect burden of disease as components of the overall burden of disease. He will also provide a comparative perspective as to how this information can be utilized by US value assessment and payers. Jan McKendrick will spend 10 minutes discussing providing a comparative perspective as to how measuring the indirect burden of disease can be useful as part of the evidence submissions in ex-US HTA submissions and how these evidence requirements may vary from country to country. Karl-Johan Myren will spend a 10 minutes sharing the perspective of the manufacturer on the pragmatic application of utilizing data on the indirect burden of diseases in EU HTA submissions. and the importance of indirect disease burden measurement for patient-centered drug development.
Laura Housman, MPH, MBA, DrPH(c)
Avalere, Washington, DC, USA
Jan Mckendrick, BSc MSc PGDip
Avalere Health, Knutsford, CHE, United Kingdom
Karl-Johan Myren, MSc
Alexion AstraZeneca Rare Disease, Stockholm, Sweden
Taylor Schwartz, MPH
Dr. van Til will provide examples of frequently used methods identified in the SIG's systematic literature review and discuss how attribute importance and preferences for options are quantified. Interactive polling will be used to elicit attendees’ attitudes about using preference elicitation methods as VCM, then compare attendee attitudes with the evidence in literature.
Dr. Poulos will discuss development of a VCM using object-case best-worst scaling to support patient decision making on treatment of primary immunodeficiency diseases. She will describe how the formative research process influenced the design of the PtDA.
Ms. Thomas will outline how multi-dimensional thresholding can be used as a VCM with patient-specific outputs provided in a PtDA. She will walk through an example of the process for a patient and the types of outputs that could be provided.
Speakers will address the potential of preference elicitation methods as a VCM, areas requiring additional research (eg, recommending a treatment option based on preferences), and challenges (eg, in generating individual preference estimates with some methods). Audience discussion will follow the case study examples. Presented by the ISPOR Health Preference Research Special Interest Group
Janine van Til, PhD
University of Twente, Enschede, Netherlands
Christine Poulos, PhD
RTI Health Solutions, Research Triangle Park, NC, USA
Caitlin Thomas, MSc
Evidera, London, LON, United Kingdom
This educational symposium aims to delve into the methodologies, processes, and results related to HTA assessment specifically for diagnostic imaging technologies. We will review the results from research and explore whether existing methodologies adequately capture the full value of these diagnostic technologies.
After the presentation, a panel with stakeholders representing different perspectives will engage in a debate to discuss the benefits and drawbacks of different HTA approaches for diagnostics to identify the suitability of current frameworks and areas for improvement.
Join us as we explore the intersection of HTA, evidence, and healthcare outcomes in the context of diagnostic imaging.
GE Healthcare
Ricardo De La Fuente, PharmD, PhD, MBA
GE Healthcare, Arlington Heights, IL, USA
Analysis Group
Eric Q. Wu, PhD
Analysis Group, Inc., Boston, MA, USA
Jimmy Royer, PhD
Analysis Group, Inc., Montreal, QC, Canada
Song Wang, PhD
Takeda, Singapore, Singapore
Guidehouse
LCP Health Analytics
Sreeram Ramagopalan, PhD
Lane Clark & Peacock, London, LON, United Kingdom
Charlotte McIntyre, MD
Medicines and Healthcare products Regulatory Agency, London, United Kingdom
The session will be dedicated to showcase good practices for minimizing gender biases in companies within the healthcare sector through examples of startups led by women. One example will be an innovative startup whose mission is to eradicate infertility, a globally prominent condition that affects approximately 200 million people and causes significant financial, psychological, and physical strain on patients. Another example will come from the experience of a startup advancing in the validation and market launch of a diagnostic test for the improvement of endometrial cancer diagnosis, representing an innovative solution in women's health.
The Healthcare Businesswomen’s Association (HBA) is the global leader in gender equity, advancing women into positions of influence and decision-making to improve health equity and outcomes.
The organization is comprised of individuals and organizations in healthcare committed to:
This session will include a panel of Marina Rigau, CEO, Mimark; Rita Vassena, CEO, Fecundis; and Ester Guiu, Director - Business & Launch Excellence, Global Commercial Operations, Almirall.
Robert Fabregat, PhD
BIOCAT, Barcelona, Barcelona, Spain
Steffen Wahler, MD; MA
St. Bernward GmbH, Hamburg, Germany
OBJECTIVES: After introducing biosimilars, expectations were high for dramatic cost reductions for biologics. However, savings have been modest in the US. This may be due to the US reimbursement model. For physician-administered drugs, which includes many biologics, providers receive payment based on the percentage of the cost billed, incentivizing the highest-cost product. Currently, US policymakers are debating changing this policy. However, to understand changing the system, causal estimates of the savings seen in the current system are needed, which this study aims to provide.
METHODS: To model cost-savings associated with biosimilar introduction, we used Bayesian structural time series models (BSTS), which utilize information from control time series data that closely resembles biologics’ ASP trends before biosimilars’ market entry. We identified prices from Medicare Part B Drug Average Sales Price (ASP) Pricing datasets (2005 Q1 - 2024 Q2). We included the following biologics: bevacizumab, epoetin(ESRD and non-ESRD), filgrastim, infliximab, peg-filgrastim, rituximab, and trastuzumab. We identified a set of control medications for each biologic such that the controls’ ASP trends closely resemble those of their respective biologics and are unlikely to be influenced by biosimilars’ market entry. We generated the predicted counterfactual ASPs, accompanied by 95% prediction intervals. Since the included biologics had different post-biosimilar entry times, we reported the cumulative price difference between biosimilars and the corresponding parent biologic over 3-year, and all post-biosimilar times.
RESULTS: Except for filgrastim, all biologics had a significant decrease in their ASP per dose values after the entry of biosimilars. Three years after biosimilar entry, most biologics ASP per dose decreased by 10 to 19%, with epoetin (ESRD) and epoetin (non-ESRD) having the greatest reduction in ASP per dose: -19.3% [-25.1%, -6.3%] and -19.6% [-25.5%, -6.7%], respectively.
CONCLUSIONS: Savings from biosimilar introduction are modest. Policymakers should consider alternative reimbursement approaches that align provider and payer incentives.
OBJECTIVES: Biosimilars hold much promise for healthcare systems by improving patient access to efficacious and established treatments whilst mitigating costs. Despite biosimilar policies implemented across the EU, the effects of these measures remains unclear due to complex interplays between prescribing requirements, switching guidelines and tendering. This study aims to assess the impact of biosimilar policies on pricing dynamics in France, Germany, Italy, Spain and the United Kingdom.
METHODS: A targeted literature review was conducted to explore policies, prescribing guidelines and reimbursement requirements for biosimilars across Europe. Following this, launch dates and list prices of biosimilars approved by EU in last 5 years were extracted from NAVLIN database. Analysis was conducted to assess the correlation between biosimilar policies and prices.
RESULTS: Across majority of scope countries, HTAs are not required for biosimilars, and switching is at physicians' discretion. However, in Italy, HTAs are mandatory, and prescribers must adhere to guidelines. In-patient setting tendering is applied in all counties but only France, Italy and Spain have regulated pricing policies. Financial models including benefit sharing models and prescription quotas were mostly implemented in Germany, France and the UK. From Jan 2019 till May 2024, a total of 17 biosimilars are available in the EU. The average per pack launch price ranged between €581 to €1,128. Biosimilars in UK experienced the highest price erosion (~60% discount from launch price), followed by France and Germany with 20% and 10% discount, respectively.
CONCLUSIONS: Despite relaxed price-setting in the UK, biosimilars there experienced the most significant price erosion compared to other European markets, likely due to centralized negotiations and switching polices mandated by NHS directives. To fully understand the underlying impact of these policies, further research is needed to investigate policy implementation at the sub-national level, particularly interactions between institutions, physicians, and pharmacists.
OBJECTIVES: Many countries use External Reference Pricing (ERP) – also known as International Reference Pricing (IRP) – to set the price of medicines. The formula used to set the price in the referrer country in relation to price of the same medicine in a basket of reference countries is one of the most important elements of the ERP system. This study aims to assess the variation in formula use globally.
METHODS: The IRP systems of 70 countries were examined using the GlobalData IRP 360 country profiles and IRP Matrix. Countries were split into groups based on their ERP formula archetype.
RESULTS: Referencing the lowest price among the basket of reference countries is still a popular formula – currently used by 22.9% of countries. An additional 12.8% of countries reference the Nth lowest or the average of N lowest prices. Some 31.4% reference the average, 1.4% – the median, and 2.9% – the highest. An astounding 12.8% of countries have dual formulas, meaning that a different formula would apply to different drug types or in case the price is calculated from neighbouring vs developed countries in the reference basket. Another 7.2% use formulas including “or”: for example, the average/median from the basket of reference countries or the price in a specific country, whichever of the two is the lower/average. For two other countries the formula is unspecified. Just one country sets its reference price under ERP at a percentile of the available prices. Notably, one country applies a different, more punitive, formula for drugs with a waiver from pharmaco-economic evaluation.
CONCLUSIONS: Formulas used the calculate the price under ERP are increasingly complex, with a significant proportion of countries applying a different formula for different therapeutic areas or for domestically produced originator medicines. Departure from referencing the lowest price improves alignment with ERP best practice guidelines.
OBJECTIVES: Equitable pricing aims to maximise access to innovation by pricing it according to the country’s willingness to pay (WTP). The objective is to investigate whether the current International Reference Pricing (IRP) practices promise fair pricing for countries in Europe or not.
METHODS: Recent IRP rules for 29 European countries were extracted from the literature. The ex-factory launch prices of the top 10 cancer drugs globally by revenue were extracted from countries' national price formularies or from pricing intelligence platforms. The gross-national income per capita (GNI/capita) was extracted from the World Bank and used as a proxy for WTP of countries. The comparative analysis included graphing the basket GNI/capita of each country against its own GNI/capita to observe if countries are pricing their drugs by referencing comparable countries or not. In a combined graph, the daily defined dose (DDD) ex-factory launch price of the 10 drugs was standardised to their mean for each country (scatter plot) and graphed against the GNI/capita of each country (bar chart) to compare the relative launch price ratio between 29 countries.
RESULTS: The basket GNI/capita of each country against its own GNI/capita showed that some wealthy countries attempted to “free-ride” by referencing mostly from countries of lower income. Most IRP formulas used the mean, which enabled some countries to price innovation below their actual WTP. The coefficient of variation of the relative launch price of DDD of 10 drugs across 29 countries was calculated at 2.22%. This low variation persists despite significant differences in GNI/capita among these countries (example: GNI/capita of Austria: $70,9k, while Bulgaria: $34,3k).
CONCLUSIONS: Though IRP is widely used in Europe as a cost-saving tool, it is vital to address the potential inequalities that arise from applying IRP. Redesigning IRP or navigating other policies that capture a country's WTP and value of innovation should be explored.
Sofie Berghuis, PhD
GSK, Amersfoort, Netherlands
OBJECTIVES: Transportability analysis can facilitate health technology assessment (HTA) decision making by transporting the findings from randomized clinical trials (RCTs) or observational studies to a distinct target population. Recently there have been rapid developments in transportability analysis methods, but adoption of these methods is still in the early stages. We conducted a landscape review of applications of transportability analysis to gain an overview of how transportability analysis is being used in practice.
METHODS: We systematically searched PubMed for articles about transportability analysis. We then screened the abstracts and full texts of the returned articles to only include those that applied at least one transportability analysis method on real datasets collected from human populations. We extracted data from the screened articles on the clinical context, the characteristics of the source and target datasets, and the characteristics of the statistical analysis.
RESULTS: The PubMed search returned 1439 abstracts, from which we retained 90 for full text screening. We ultimately included 52 articles, three of which were identified through hand-searches. Among these articles, 81 separate transportability analyses were performed on 62 source/target dataset pairs. Among source datasets, 42 (67.7%) were from US populations, and 51 (82.3%) were from RCTs, with 8 (12.9%) from observational studies. Among target datasets, 40 (64.5%) were from US populations, and 41 (66.1%) were from observational studies, with 18 (29.0%) from RCTs. Most analyses were conducted using individual patient data (IPD) in both the study and target datasets. Weighting (43 analyses, 53.1%), g-computation (13 analyses, 16.0%), and augmented approaches (15 analyses, 18.5%) were most frequently used to conduct transportability analysis.
CONCLUSIONS: Applications of transportability analysis have not substantially expanded beyond US populations. More analyses which transport findings to non-US populations will be helpful in establishing the role of transportability analysis in global health.
OBJECTIVES: Randomized clinical trials (RCTs) are an important source of evidence for health technology assessment (HTA), but their external validity is often uncertain. Assessment of RCT external validity can facilitate decision making by improving the relevance of RCT evidence to local contexts. Transportability analyses are causal inference methods used to assess the validity of RCTs in external settings. We conducted a simulation study to assess the performance of two novel methods, inverse odds of participation weighting (IOPW) and g-computation, for transporting original RCT findings to real-world data (RWD).
METHODS: Datasets from RCT (study) and RWD (target) populations were generated with different distributions of covariates and effect modifiers and different true average treatment effects (ATEs) for a continuous outcome. We assumed that the treatment and response variables were only observed in the study dataset. IOPW and g-computation methods were each used to estimate the ATE in the target population using its covariate data. Bias, coverage of 95% confidence interval (CI), and relative error of standard error (SE) were used to compare the performance of IOPW and g-computation versus naïve linear regression.
RESULTS: Compared to the naïve estimator, the IOPW and g-computation estimators performed considerably better on all metrics. There was considerably less bias, and the 95% CIs of the target ATE calculated using IOPW and g-computation empirically met the nominal level. The relative errors of SE were small in all methods. The performance of the transportability analysis methods is contingent on specifying the models correctly when using IOPW and g-computation.
CONCLUSIONS: Transportability analysis is an important tool that can facilitate HTA decision making. As seen in our simulation study, transporting results from RCT to real-world settings using IOPW and g-computation methods show great promise. Challenges in the analysis include correctly specifying factors that affect treatment assignment in the RCT and modify the treatment effect.
OBJECTIVES: Cost-effectiveness models typically use reported aggregate utility values from scientific literature. Ideally, these models should incorporate country-specific utility values. We introduce a novel copula-based simulation method to estimate mean EQ-5D utility values for any value set using reported mean values and the original value set. We demonstrate the usefulness of this approach by comparing estimated and observed mean values for arthritis and diabetes patient groups in the Multi Instrument Comparison study, using UK crosswalk values as input to estimate German and Spanish mean utility values.
METHODS: We generated EQ-5D data using a copula approach to reflect a natural distribution of responses modeled on self-reported health states in a mixed patient population. The modeled data included EQ-5D-5L questionnaire and EQ-VAS responses, maintaining correlations between dimensions and cumulative densities. The resulting simulated dataset was then re-weighted to produce mean values matching the target value in the target value set. Finally, mean values for alternative value sets were estimated for using the simulated data and fitted weights.
RESULTS: In our MIC data analysis, the observed mean utility values for arthritis patients were 0.626 (UK crosswalk), 0.733 (German 5L), and 0.697 (Spanish 5L). Our simulation estimated values of 0.742 (German) and 0.693 (Spanish). For diabetes patients, observed values were 0.695 (UK crosswalk), 0.793 (German 5L), and 0.754 (Spanish 5L), with estimated values of 0.799 (German 5L) and 0.754 (Spanish 5L). Across all disease groups, the mean absolute error of estimation was approximately 0.005.
CONCLUSIONS: Our copula-based simulation method provides reliable and precise estimates of EQ-5D utility values for different country-specific value sets using reported aggregate means from one value set. This approach can be further refined using empirical EQ-5D data from similar patient populations.
OBJECTIVES: Transportability methods provide a tool to extend results from randomized controlled trials or observational studies to real-world populations, typically relying on individual-level data. This study sought to validate and compare existing transportability methods that use aggregate data from target populations in the context of a different therapy area.
METHODS: Using methods previously tested for a cardiovascular prevention trial, we extended a treatment effect from the NIDA-CTN-0044 trial to a target population of U.S. substance abuse treatment seekers, represented by the 2021 Treatment Episode Data Set (TEDS): Admissions dataset. Three methods using aggregate data from the target population (weighting using simulated individual data, weighting using method of moments, and post-stratification) were compared against the gold-standard method: weighting using individual-level data. Transported odds ratios were reported for each method alongside 95% confidence intervals using robust standard errors (except for post-stratification). Seven common covariates were included as categorical or binary variables.
RESULTS: The information reported here results from secondary analyses of data from clinical trials conducted by the National Institute on Drug Abuse (NIDA). Specifically, data from NIDA-CTN-0044 Web-delivery of Evidence-Based, Psychosocial Treatment for Substance Use Disorders were included. A total of 406 participants in the NIDA-CTN-0044 study and a target population of 350,536 in the TEDS were identified. Weighting using method of moments yielded estimates closest to the gold-standard estimate transported the trial. For the NIDA-CTN-0044 trial, the expected treatment effect (odds ratio (OR)=1.32, 95% CI 0.76-2.28) obtained for the target population using the gold-standard approach was mostly closely approximated by the method of moments (OR=1.31, 95% CI 0.70-2.43), followed by the simulated data approach (OR=1.28, 95% CI 0.72-2.29) and post-stratification (OR=1.46).
CONCLUSIONS: Weighting using method of moments provided transported estimates most comparable to the gold-standard method. Future research should further validate aggregate-based transportability methods across different therapy areas and settings.
Shahid Hanif, PhD, MSc
GetReal Institute, Utrecht, Netherlands
Kapil M Khambholja, PhD
Catalyst Clinical Research, Thiruvananthapuram, Kerala, India
Jessica Santos, PhD, Ma, Bsc, CIPP
Oracle Life Sciences, Cambridge, Cambridgeshire, United Kingdom
Sebastian Heidenreich, PhD
Divya Mohan, PhD
OPEN Health, London, London City, United Kingdom
Paul Schneider, MD, PhD, Msc
Valorem Health, Bochum, NRW, Germany
PURPOSE: Health Technology Assessment (HTA) bodies increasingly consider supplementary evidence from real-world data (RWD) alongside traditional data sources like RCTs. However there remain concerns about bias and the applicability of RWD from other countries to the local settings. Further complicated by the uneven availability of high-quality RWD across different geographical regions. This workshop will discuss how transportability analysis, like methods used to adjust for confounding factors, can help bridge this gap. Recently NICE updated their real-world evidence framework to include transportability methods as a potential approach to adjust for external validity concerns.
DESCRIPTION: Workshop attendees will hear from different stakeholders across the industry including decision makers on how transportability methods can address the lack of quality local data within an HTA submission. The workshop will a) present a transportability framework and methods, and current stakeholder guidance and b) address how transportability methods can be applied in practice. Philani Mpofu will open the workshop with a brief introduction on the concepts (5 min). Alexandra Starry will draw learnings from previous NICE and G-BA appraisals and introduce a transportability analysis framework (15 min). Marian Eberl will discuss challenges from the industry perspective to address requirements for local data and present a pilot transportability analysis in lung cancer using US and German data as an example explaining how the study was adapted to the previously outlined framework (15 min). Stephen J. Duffield will discuss key considerations for the acceptability of international data and transportability analysis approaches from a decision maker perspective (10 min). Then Philani Mpofu will lead the audience participation, by walking them through a hypothetical case study, asking them to identify differences that should be considered across a multi-country transportability project (15 min). This interactive and informative workshop will inform researchers and industry analysts on how to design their own transportability study.
Philani Mpofu, PhD
Marian Eberl, MSc
Daiichi Sankyo Europe, Munich, Bavaria, Germany
Alexandra Starry, MPP
Cytel, Berlin, Germany
OVERVIEW: The session, moderated by Jag Chhatwal, will begin with an exploration of the current generative AI landscape and its intersections with HEOR. Rachael Fleurence will illustrate practical applications, as well as limitations, of generative AI in systematic literature reviews, evidence synthesis and real-world evidence. Xiaoyan Wang will discuss the potential of generative AI in developing synthetic or external control arms, which could significantly expedite the evidence generation process for clinical trials. Jag Chhatwal will present the current uses of generative AI in health economic modeling, highlighting both the current capabilities and challenges. Finally, Dalia Dawoud will discuss how HTA may adapt evaluation methods and processes in response to submissions that employ generative AI.
The session will feature interactive polling to engage attendees actively and gather their insights. By bringing together diverse expert views, this session aims to foster a comprehensive understanding of the multifaceted roles, ongoing challenges, and future prospects of generative AI in HEOR. It will also address the existing uncertainties and skepticism surrounding the adoption of generative AI in HEOR.
National Institute of Health and Care Excellence, London, United Kingdom
National Institutes of Health, Bethesda, MD, USA
IMO health, Rosemont, IL, USA
Chris Cameron, MSc, PhD
EVERSANA, Sydney, NS, Canada
Shannon Cope, MSc
Precision AQ, Vancouver, BC, Canada
Justin Doan, MSc, MPH, DrPH
Pfizer, Inc., New York, NY, USA
ISSUE: The role of HEOR and Market access is growing in importance. We are witnessing many reorganizations across pharma companies with the aim of better integrating access into the overall product strategy. Tatia Woodward and Indranil Bagchi, representing respectively global HEOR and Market Access, will discuss how these functions need to evolve and collaborate to create a holistic strategy that demonstrate the value of innovative products from pipeline until patent expiration. They will share their direct experience in terms of the impact and evolution of their roles, as well as perspectives on what more is needed to elevate the access function within the company. Brian O’Rourke will provide the HTA perspective in terms of evidence required for value demonstration and highlight how institutions like ISPOR can support this evolution.
OVERVIEW: We are witnessing a fundamental transformation of the global HTA landscape. Center for Medicare and Medicaid Services (CMS) is empowered to negotiate drug prices, the EU is creating a singular, comprehensive HTA process, Canada and the UK are updating their frameworks focusing on sustainability and innovation, and collaborations such as the Access Consortium are emerging in various markets. The need to demonstrate the value of new medicines, vaccines, and devices across the entire product lifecycle is becoming more urgent than ever. Real World Evidence (RWE) has emerged as a pillar to demonstrate effectiveness, long term safety, durability, and risk/benefit profile in populations not included in clinical trials, though acceptance of RWE across stakeholder groups remain variable. Value-based pricing is becoming a standard access strategy at launch, for expensive medications that obtain accelerated regulatory approval. The need to reconsider clinical trials program design, often designed just for fast regulatory approval, is forefront to allow collection of data also necessary to inform optimized access and pricing.
AESARA Inc., Madrid, M, Spain
Indranil Bagchi, PhD
GSK, Philadelphia, PA, USA
ISPOR, Lawrenceville, GA, USA
Tatia Chay Woodward, MPH, MSc
ISSUE: More clinical trials and real-world evidence studies are using digital endpoints, which are outcomes collected using digital tools such as wearables. These endpoints can provide useful information on how patients are functioning between clinic visits and do not rely on patient recall or opinion. There is little experience with them, however, so lack of validation and uncertainty over patient compliance mean they are not commonly considered by HTA bodies. This panel will discuss whether there are any scenarios where digital endpoints are ready to be considered in HTA, what the pros and cons are of using digital endpoints, and what researchers and HTA bodies need to do to allow digital endpoints to be used for decision making in the future.
OVERVIEW: Each panellist will have 10 minutes to outline their case. This will be followed by a debate amongst panellists, moderated by Jeanette Kusel. The audience will have the opportunity to pose questions to the panellists and polls will be used to elicit audience opinion. Dr Jacoline Bouvy will explain why HTA bodies are excited about the prospect of digital endpoint, but currently cautious about their use to inform decisions. She will discuss what is needed in terms of validation and links to health-related quality of life for HTA bodies to be more reassured of their use. Prof Lynn Rochester will highlight recent efforts that support cases where digital endpoints have been developed, validated and can be used in decision-making, highlighting the case study of the Mobilise-D initiative. Dr Kai Langel will highlight the work that is ongoing through the DEEP initiative to make digital endpoints fit for purpose for decision making. This panel will be of interest to all those involved in technology development and evaluation.
Kai Langel, BSc
Deep Measures, Helsinki, Helsinki, Finland
Lynn Rochester, PhD
Newcastle University, Newcastle, United Kingdom
Kate Hanman, BSc, MSc
Costello Medical, Boston, MA, USA
Annabel Griffiths, PhD
Formerly at Chronic Granulomatous Disease (CGD) Society, Dartford, Kent, United Kingdom
Mohit Jain, PhD, MBA
BioMarin Pharmaceutical, London, United Kingdom
Arjan Lankester, PhD, MD
Leiden University Medical Center, Leiden, South Holland, Netherlands
Min Huang, PhD
Merck & Co., Inc., Rahway, NJ, USA, Rahway, NJ, USA
Michael Drummond, PhD
University of York, Lichfield, United Kingdom
Guido España, PhD
Centers for Disease Control and Prevention, Atlanta, GA, USA
Rachel Oidtman, PhD
C. Daniel Mullins, PhD
This session will analyze whether these long-term outcomes justify continued access to CAR-T and cell therapies, considering both clinical results and cost-effectiveness data, and how OBAs may evolve to support sustainable access moving forward.
Lifescience Dynamics
Alberto Briones, BSc, PhD
Lifescience Dynamics, London, United Kingdom
Network meta-analysis is the most widely used ITC approach, under the assumption that any variables that modify treatment effects are balanced across the included study populations. Recent population-adjusted ITC methods aim to relax this assumption, adjusting for differences between populations using individual participant data from one or more studies. Despite its limitations – such as requiring and being sensitive to population overlap, being restricted to estimates in aggregate study populations, and being limited to two-study indirect comparisons – matching-adjusted indirect comparison (MAIC) remains the most commonly used population-adjusted ITC approach. However, poorly conducted ITCs can have significant consequences, potentially leading to a potential cost-effective intervention not being reimbursed.
This session will explore innovations in ITC methodologies and introduce alternative approaches to MAIC, including simulated treatment comparison (STC) and multilevel network meta-regression (ML-NMR), with a special focus on applications to time-to-event outcomes. Case studies will be presented to highlight the limitations and pitfalls of MAIC, while demonstrating how alternative methods can address these limitations.
Attendees will gain a comprehensive understanding of the diverse range of ITC approaches and acquire practical knowledge of the key principles needed for performing robust ITC analyses.
BeiGene
David Phillippo, PhD, MSc
Shijie Ren, PhD
Rhys Williams, PhD
BeiGene, Las Vegas, NV, USA
Recommended single cancer screening tests save lives, but this technology still only exists for a select number of cancer types. Instead of looking for cancer one-at-a-time, organ-by-organ, multi-cancer early detection technology has the ability to screen individuals for multiple cancers in a cost-effective manner. Adding these new technologies to single-cancer screening has the potential to change the cancer landscape and improve patient outcomes by helping to identify cancers at earlier, more treatable stages on a population scale.
Grail, Inc
DESCRIPTION: The vision of the Women in HEOR initiative is to support the contribution of women in our field by serving as a catalyst for leadership and a platform for mentorship, collaboration, and networking. This Women in HEOR Forum will bring into focus gender equity as it relates to the health technology development pipeline. Panelists will address several topics in early phases of development such as the role of funding support of women’s health research, pharmaceutical manufacturer investments in women’s health products/treatments, regulation policy to ensure gender-appropriate data collection and research to identify unmet medical needs among women. Panelists will also consider later phase topics such as research that examines women’s access to treatments, reporting guidelines to consider women in health research, and representation in clinical trials.
The agenda for the session will include: 1) an introduction to the Women in HEOR initiative and its objectives and activities 2) brief remarks by each panelist, and 3) a moderated discussion of the above topics throughout the healthcare technology development pipeline. The panelists will engage with the audience through live polling and open Q&A. Attendees will gain valuable perspectives on gender equity perceived challenges, potential solutions, and current initiatives to address the issue. This session is open to all conference attendees.
Olivia Wu, PhD
Takeda, Boston, MA, USA
Grammati Sarri, PhD, MSc, DiDS
Cytel, London, United Kingdom
Julia F. Slejko, PhD
University of Maryland Baltimore, Baltimore, MD, USA
OVERVIEW: Jan Hansen (Genentech) will moderate this session and set the context by explaining global shifts in HEOR organization and investment. Rob Abbott (ISPOR) will present the importance of measuring HEOR’s impact in the context of ISPOR’s Strategic Plan 2030.Laura Pizzi (ISPOR) will discuss the impact measures that ISPOR currently uses, and opportunities to improve them. Lotte Steuten (OHE) will discuss ways to measure the impact of HEOR on methods guidelines, healthcare policies and patients, using the example of an analysis with BMJ Impact Analytics that tracked the trajectory and use of HEOR research in real-time. James Chambers (Tufts University) will discuss the impact of HEOR on coverage decisions using data from the Tufts Medical Center Specialty Drug Evidence and Coverage (SPEC) Database. Drawing on these examples, panellists will debate the approaches to HEOR impact measurement, including the role of data-driven methods and AI-tools, and establish a call to action for the use of these measures as well as development of new measures.
Jan E. Hansen, PhD
Genentech, South San Francisco, CA, USA
James Chambers, PhD
Genentech, Inc., Boston, MA, USA
In March 2024, the European Commission ratified methodological guidelines specifying the statistical analyses for the new Joint Clinical Assessment (JCA). The guidelines address the inclusion of SATs and external controls through discussion on methods for analysing non-randomised data and disconnected networks.
However, the pre-conditions for the acceptance of non-randomised comparisons, which include pre-specification of the analysis methods, are not without their challenges, especially given the time available for the JCA. Moreover, such pre-conditions are likely to differ between EU member states.
With the new regulation taking effect in January 2025, there is an imperative to clarify the conditions under which the use of non-randomised evidence from SATs in benefit dossiers will be accepted. This session will serve as a pivotal platform for fostering dialogue between HTA bodies and the industry regarding expectations and achievable outcomes.
Mr. Scott will outline the current guidelines regarding the use of SATs and external controls, as well as offering insights into the ongoing discourse surrounding their acceptance. Dr. Jen, Dr. McConnell, and Dr. Ren will put forward their position regarding the conditions under which treatment comparisons from SATs using external controls can be relied upon for the purpose of providing evidence of additional benefit. The remainder of the session will be left for debate with the audience on the expectations and efforts required for HTA submissions, considering the challenges of clinical trial design, development costs, ethical considerations, and the imperative to expedite patient access to effective medications.
Martin Scott, MSc
Numerus, Reutlingen, Germany
David McConnell, PhD
National Centre for Pharmacoeconomics, Dublin, Ireland, Dublin, Ireland
University of Sheffield, Sheffield, South Yorkshire, United Kingdom
In this presentation, we will explore approaches to automating model-building and required statistical analyses and introduce an innovative modeling platform. This platform automates model generation for a broad range of therapeutic areas and model types using a wizard for capturing model specifications and creating customized models while minimizing errors. Once the generated model is populated with inputs, the platform can run the model on a PC or a server-based environment to optimize run-times. Automation of the statistical analyses that inform model inputs will also be described and illustrated with an application for parametric fitting of time to event equations. Finally, these automation tools will be demonstrated to the audience by using the platform.
During the Q&A period, the advantages and challenges of automated model building and contrasts to AI-based model generation will be discussed.
Discover how an automated model-building tool can significantly expedite the creation of early health economic models that adhere to good practice guidelines and minimize errors. Furthermore, learn how automation tools leverage the computational power of server-based installations while preserving the Excel-based nature of models preferred by stakeholders.
Evidera
Ágnes Benedict, MSc, MA
Evidera, a part of Thermo Fisher Scientific, Vienna, Austria
Apoorva Ambavane, MSc, MPH
Evidera, Inc, London, United Kingdom
K. Jack Ishak, PhD
Evidera, St-Laurent, QC, Canada
PT43 Implementation of a Health Technology Assessment Unit: Experience in the Largest Private Healthcare Provider in Oncology in Brazil
PT44 Bridging HTA Assessment and Policy Appraisal and Decision
PT45 Don't Sleep on HTA: From HTA to Clinical Guidelines Within Treatment of Obstructive Sleep Apnea
PT46 Development of Economic Evaluation Studies (EEs) in Saudi Arabia
PT47 The Use of Real-World Data to Reduce Uncertainty and Turn a Negative NICE Decision to Positive in the Presence of Pronounced Health Inequalities: Lessons Learnt From the NICE Appraisal of Voxelotor for Sickle Cell Disease
PT48 Use of an Individual-Based Approach in Oncology: A Case Study for the Treatment of Pediatric Patients With Gliomas
PT37 Comparison of ICD-10 Diagnoses and Longitudinal Estimated Glomerular Filtration Rate (eGFR) in the Detection of Incident Stage 3 Chronic Kidney Disease (CKD): A Retrospective Comparison Study Using UK Secondary Care Data
PT38 Is There an Increased Risk of Developing Hypertension After a COVID-19 Infection?
PT39 A Mixed-Methods Blueprint for Obesity Healthcare Policy: From Research to Action
PT40 Assessing the Value of Real-World Evidence for HTA-Decision Making
PT41 Benefits of Inhaled Corticosteroids (ICS) in COPD Maintenance Combinations: Real-World Evidence Using Longitudinal Targeted Maximum Likelihood Estimation
PT42 Disease Burden and Unmet Medical Need in Patients With Ulcerative Colitis in Greece: A Cross-Sectional Patient Survey
Isaac Corro Ramos, PhD
Institute for Medical Technology Assessment, Rotterdam, Netherlands
OBJECTIVES: Manual chart review used to generate real-world data can be long and expensive. We used an innovative approach based on an artificial intelligence natural language processing (AI-NLP) method to structure data into a cohort of patients affected by chronic Graft Versus Host Disease (cGVHD) following hematopoietic stem-cell transplantation (alloHSCT).
METHODS: The NLP scanned all electronic medical records, identified eligible patients and used relevant information to derive and infer the variables.
RESULTS: From all alloHSCT patients’ medical files (N=306), 20 met the selection criteria, whose data were extracted with NLP. This comprehensive process took 30 hours by a data scientist and 0.5 hour of investigator expert time per center.
CONCLUSIONS: This innovative AI-NLP process allows to generate a comprehensive, robust and accurate structured dataset, from scattered information of all eligible patients, in medical records, with minimal human intervention.
OBJECTIVES: To describe primary care resource utilisation and costs of index and imminent subsequent fragility fractures in postmenopausal women using electronic medical records from the United Kingdom (CPRD-Aurum), the Netherlands (IPCI), Spain (SIDIAP), Germany (IQVIA-DA), and France (IQVIA-LPD) mapped to the OMOP-CDM.
METHODS: A time-stratified propensity-score-matched study identified three cohorts of women aged ≥50 in 2010-2018: imminent subsequent fracture within two years of their index fracture (F2); index fracture with no fracture two years prior (F1); and without fractures (F0). Consultation counts by staff roles (CPRD), specialty (SIDIAP, IQVIA-DA and IQVIA-LPD), and visit types (IPCI) were extracted and costs estimated based on local tariffs. The impact of first (F1 vs. F0) and subsequent fractures (F2 vs. F1) was described between the matched cohorts.
RESULTS: For F1 vs. F0, data from 343,573 and 1,096,675 women, respectively, were used across all databases. For F2 vs F1, samples comprised a total of 97,750 and 316,451.
CONCLUSIONS: Fragility fractures are associated with an increased number of primary care consultations and associated costs, with imminent subsequent fractures generally furthering this burden. This is the first multi-country European study describing resource utilisation and costs amongst postmenopausal women with fractures using data mapped to OMOP-CDM.
OBJECTIVES: When automated electronic health record (EHR) databases are insufficient or unavailable, Health Care Professional survey-based chart audit (HSCA) offer a valuable alternative for health outcomes research studies. However, limited information exists regarding the comparison between HSCA data and EHR data.
METHODS: This was a secondary analysis of 2 multi-country post-authorization safety studies, one HSCA and an HER databases study, both aiming to collect drug utilization data to assess the effectiveness of risk minimization measures related to the use of an antiepileptic drug among female patients. Patient data from 6 HER databases (general practitioners (GP) panel in France, UK, Germany and Spain; neurologists/psychiatrists (NP) panel in Germany and Spain) were compared to HSCA data collected during the same period over the same physician specialties in four countries. Two age groups (females of child-bearing potential (CBP), and non-CBP) and 3 possible indications (epilepsy, bipolar disorder, migraine) were defined. Distribution of age groups and indications in both study populations was compared using EHR data as reference.
RESULTS: The non-CBP age group was predominant in all panels (ranging from 50.5% in UK GP HSCA to 67.3% in Germany GP EHR) except in Spain NP panel (45.5% in EHR and 46.6% in HSCA). Epilepsy was the first indication recorded in all panels (ranging from 58.7% in Spain GP HSCA to 96,3% in Germany GP EHR) except in Spain NP panel (29.9% in HER and 18.8% in HSCA). These was no significant differences for age group distribution between the 2 sources in France GP, Germany and Spain NP panels. Proportion estimates for indications differed between the 2 sources in the majority of the panels.
CONCLUSIONS: Differences in data collection methodology can explain the discrepancies observed between the 2 sources, however, the use of HSCA can generate valuable insights on routine clinical practice.
OBJECTIVES: The study assesses the contribution of using free-text from electronic medical records (EMR) by large language models (LLMs), to the completeness and validity of information on medication-use. We present an example of data on aspirin intake during pregnancy.
METHODS: Data on medication use (Acetylsalicylic Acid, “Aspirin”) was derived from available EMR sources, coded and free-text fields in several hospitals within Briya’s network. Medication listed in coded fields was identified using ATC codes. Free-text analysis of EMR from the documenting departments (emergency room, delivery room, high-risk and post-delivery wards) was performed using on-premise open-source LLM (LLama 3-8B). Frequency of aspirin use was described by source of data, department and hospital. Sample of notes were reviewed by human experts for validation of LLM and F1 score, precision and recall (sensitivity) were calculated.
RESULTS: The study population included 16,122 women who gave birth to 19,185 infants (18,836 deliveries) between 03.2020- 05.2024. 1,142 women (6.1% of deliveries) used aspirin during pregnancy. Positive aspirin use was documented 1,596 times, 1,111 in free-text (range 1-6 notes/women). Compared to experts, LLM validation metrics were: F1=0.9; precision=0.88, recall=0.92). About 66% of patients who received the medication (n=755) were identified exclusively via free-text, another third (n=356) were identified by both free-text and coded fields, resulting in free-text coverage of 97.3%. The remaining 2.7% of aspirin uptake was identified exclusively in coded fields. Comparison by documenting department and hospitals, dosage, and treatment duration will be presented.
CONCLUSIONS: The use of AI for free-text data abstraction could capture the vast majority of data on medication use. LLMs demonstrate excellent zero-shot performance without task-specific training, and deployed on-premise without GPUs, optimizing hospital resources. Completeness and quality of medication-use are highly dependent on source of data.
Irina Kinchin, PhD, MSc
Trinity College Dublin, the University of Dublin, Dublin, Dublin, Ireland
OBJECTIVES: In the absence of an accepted local EQ-5D-5L tariff, NICE requires EQ-5D-5L responses to be mapped onto the long-standing EQ-5D-3L value set for the UK. Their 2022 Manual recommends using the mapping function based on the EEPRU dataset recently developed by the NICE Decision Support Unit (DSU), instead of the van Hout crosswalk that was previously endorsed. Our aim was to compare utility values derived using these two mapping methods.
METHODS: For all 3,125 unique health states existing in the EQ-5D-5L system, utility estimates were calculated using both mapping functions. Absolute differences in resulting values were calculated and a scatterplot was generated to identify outliers and allow visual comparison. Differences in mean utility values were evaluated in subsets of health states, grouped by the level of severity of reported health issues. Simulations in hypothetical patient populations were conducted to evaluate the potential impact of the mapping functions on utility values derived from EQ-5D-5L clinical trial data.
RESULTS: For 1,898 (61%) health states spread across the severity spectrum, the DSU EEPRU mapping function generates a higher utility value than the van Hout Crosswalk. The mean difference was 0.062 (± 0.139 standard deviation), ranging from +0.619 to -0.198 for individual health states. Our analyses and simulations suggest that mean utility values for a patient population estimated with the DSU mapping function would typically lie 0.050 to 0.100 higher than those obtained with the van Hout Crosswalk.
CONCLUSIONS: Our study shows that not only the choice of the EQ-5D instrument, but also the mapping approach used can have a substantial impact on utility values and, hence, estimated health gains and cost-effectiveness ratios. The DSU EEPRU mapping function tends to shift UK utility values further upwards, potentially making it harder for new technologies to demonstrate utility gains and obtain NICE recommendation.
OBJECTIVES: Proxy ratings primarily provided by informal caregivers are usually administered if people living with dementia (PlwD) are cognitively unable to rate health independently. Typically it is reported, that proxies underestimate PlwD health. However, analyses of self- and proxy-rated discrepancies in individual responses that focus on HRQoL dimensions are currently lacking. Therefore, we examined discrepancies between self- and proxy-rated health on an individual response level.
METHODS: We analyzed EQ-5D-5L baseline data of a cluster-randomized, controlled intervention trial, administered as a self- and proxy-proxy-assessment of n=174 dyads (PlwD and their caregivers). Initially, we calculated agreement statistics (weighted kappa) for each dimension of the EQ-5D. For individual response levels of each dimension, we analyzed the degree of inconsistencies between self- and proxy ratings ranging from 0 (identical responses in all dimensions) to 5 (different responses in all five dimensions). Further, we grouped the PlwD into “no problems” and “problems” depending on the EQ-5D-5L dimensions to compare the response distances between PlwD and caregivers.
RESULTS: PlwD had a mean age of 80.1 years (49.1% female) and caregivers a mean age of 67.9 years (67.8% female). Every third PlwD lived alone (62%), and 90% of PlwD were mild to moderate cognitively impaired (MMSE mean score 19.2). In general, PlwD rated their health better than proxies, with lowest kappa coefficients for usual activity (0.23) and highest for pain/discomfort (0.40). Nine dyads (5%) showed identical ratings. Among the grouped PlwD without and with problems, proxies estimated the PlwD as either worse (underestimation) or better (overestimation), coupled with more frequent mid-response options by proxies. Over- and underestimation was more present in mobility, usual activities, and self-care.
CONCLUSIONS: The two trends of contrary response behavior (over- and underestimation) of both rater types underline the need for further research when using HRQoL measures as proxy instruments and examining influencing factors in that context.
OBJECTIVES: This study aims to investigate the predictive properties between a Visual Analogue Scale (VAS) and the EuroQol EQ-5D-5L utility scores in End-Stage Liver Disease (ESLD) patients across European countries as part of the EU COPE transplantation trial.
METHODS: Complete case data was extracted from the EU COPE liver transplantation trial (n=79) across three time points. Country-specific EQ-5D-5L normative values were used to calculate utility scores. Pearson’s correlation coefficient assessed relationships between utility scores and the VAS scores. Age was the predictor for two linear regression models that explored its influence on the VAS and utility scores. A pooled analysis of all time points evaluated the predictive power of the VAS on utility scores.
RESULTS: In the UK, correlations ranged from 0.55 to 0.62 (p < 0.001). Spain and Belgium showed correlations above 0.70 (p < 0.05), while Germany had weaker correlations, especially at 30 days post-transplant (p = 0.054). Age significantly affects VAS scores in the UK, Spain, and Belgium. Utility scores demonstrated significant age effects in the UK and Belgium, but not in Germany and Spain. When assessing model fit, R2 values range from 34.6% to 41.5%, which signifies moderate predictive model given limitations on available controls and confounding factors. Therefore, it suggests that utility scores explain a moderate amount of the variability in EQ-VAS overall.
CONCLUSIONS: The study found that utility and VAS scores are associated, though the correlation strength varies across countries. Age significantly affects both measures, contributing to variation, which suggests age-related stratification is necessary for ESLD quality of life modelling. The moderate and variable predictive power of utility scores may indicate that the two techniques cannot be considered as interchangeable. Further research is warranted in understanding computational approaches addressing the impact of clinical heterogeneity for assessing quality of life.
OBJECTIVES: When health fluctuates recurrently, obtaining meaningful quality-of-life estimates is challenging. Quality-of-life changes may be missed or over/underestimated due to measures’ recall periods and timing. Using chemotherapy as a case study, we compare responses using different recall periods and timing to inform how/when to capture quality-of-life during predictable recurrent fluctuations in health; and consider the implications for other contexts with fluctuations.
METHODS: During one 3-weekly cycle of chemotherapy for cancer, patients were randomly assigned to complete:
RESULTS: 503 patients consented; all 21 daily questionnaires were completed by 84(50%), 67(40%), 72(43%) in the groups respectively.
CONCLUSIONS: Current practice of using a single estimate with a daily (EQ-5D) or longer (SF-12/SF-36) recall could bias cost-effectiveness estimates. Quality-of-life should be captured frequently with short recall when fluctuations are likely, with longer recall less frequently in more stable periods.
Malwina Holownia-Voloskova, MSc, PhD
Certara, Cracow, n/a, Poland
Gergo Meresz, Ph.D.
MediConcept Ltd., Budapest, PE, Hungary
State Expert Centre of the Ministry of Health of Ukraine, Kyiv, Ukraine, Kyiv, Ukraine
Eva Turk, PhD, MBA
Ministry of Health, Ljubljana, Slovenia
Jennifer Bright, MPA
International Consortium for Health Outcomes Measurement (ICHOM), Alexandria, VA, USA
Ian Patton, PhD
Obesity Action Canada, Quebec, QC, Canada
ISSUE: The pharmacoeconomic evidence provided by the BIA and CEA plays an important role in decision-making within healthcare systems. Despite these two instruments, systems do not always use both analyses or have different interpretations and criteria. To what extent this impacts the access to innovative medicines and how it can support their clinical assessment remains challenging. Efforts have been made to synergize the use of both BIA and CEA, for example, in a “three-dimensional pharmacoeconomic model” or “lock,” while some question the need for CEA. In the Netherlands, drugs or treatments with a high price or financial risk are placed in the lock, meaning the national authorities require both analyses for a decision. Are then BIA and CEA sufficient to address innovative medicines?
OVERVIEW: This panel will discuss the importance and the need to standardize the rules for a logical interplay between BIA and CEA. The moderator, Cornelis Boersma, will reflect on the situation (5min) and provoke debate based on several questions (15min): Is CEA always the appropriate instrument, and how far that depends on the BIA?; Could the development of a tool linking BIA and CEA support the decision-making of innovative drugs in the phase of their clinical assessment? How can industry, academia, and health authority contribute and benefit from such a tool? The panelists Josephine Mauskopf, Jaime Caro, Thea van Asselt, will address the perspectives mentioned in 10 min presentation per panelist. The audience will actively engage in the debate via online polls and open questions (10min).
Cornelis Boersma, PhD
University of Groningen, Department of Health Sciences, UMCG; Open University, Heerlen, Department of Management Sciences and Health-Ecore Ltd, Zeist, The Netherlands, Zeist, UT, Netherlands
J. Jaime Caro, MDCM, FACP, FRCPC
McGill University, London School of Economics, Evidera, Lincoln, MA, USA
Josephine Mauskopf, PhD, MHA, MA
RTI International, Research Triangle Park, NC, USA
Thea van Asselt, PhD
University Medical Center Groningen/ University of Groningen/ National Healthcare Institut in Netherlands/ The Netherlands Organisation for Health Research and Development, Groningen, GR, Netherlands
Bill Byrom, B.Sc., Ph.D.
Signant Health, Nottingham, NTT, United Kingdom
Rohini Sen, PhD
AbbVie, Arlington, MA, USA
PURPOSE: To debate whether new and emerging policies and guidance on the use of real-world evidence (RWE), should be focusing on harmonisation between regulatory and Health Technology Assessment (HTA). Through interactive discussions and real-time polling, we seek to identify the benefits and opportunities of harmonisation, but also the challenges and hindrances this could pose in regard to future policy development.
DESCRIPTION: In recent years, there has been an increasing number of policies on the use of RWE within regulatory and HTA healthcare decision-making. As more policies emerge with differing methods and recommendations, it raises the question of whether there is a greater need for harmonization between policy for regulators and HTA? Differing organizational remits of regulators and HTA agencies will likely place some limits on harmonization, but to what extent should there be a drive to harmonize other elements including between medicines and MedTech? Does harmonization support future RWE policy development or will it hinder efforts to develop policy by encouraging guidance that is too broad and non-specific?
In this workshop, Rita Peeters will introduce the topic by discussing the evolution of RWE policies across regulatory/HTA organisations (10 minutes). Katharine Cresswell will discuss the context of the Innovative Health Initiative - Integration of Heterogeneous Data and Evidence towards Regulatory and HTA Acceptance (IHI-IDERHA) project and what degree of harmonization was found in their recent global review of these policies (10 minutes). Patrice Verpillat will provide the regulatory perspective discussing perceived gaps and opportunities for future development and harmonization (10 minutes). Niklas Hedberg will discuss the HTA perspective and where harmonization may be challenging (10minutes).
A 20-minute interactive audience discussion, facilitated by real-time polling will allow discussion of critical arguments for and against harmonisation, and priority areas for future policy. Poll results will be displayed, allowing participants to see points of consensus and differentiation.
Rita Peeters, PhD, MBA
J&J MedTech, Antwerp, Flanders, Belgium
Katharine Cresswell, BSc (Int), MPH
National Institute for Health and Care Excelllence, Manchester, LAN, United Kingdom
Cytel, London, LON, United Kingdom
Luis G. Hernandez, PhD, MPH, MSc
Takeda Pharmaceuticals America, Inc., Westford, MA, USA
Seamus Kent, PhD
Erasmus University Rotterdam, Rotterdam, ZH, Netherlands
Varun Ektare, MPh
Indence Health, Thane West, MH, India
Aikaterini Fameli, PhD, MBA
European Federation of Pharmaceutical Industries and Associations, LONDON, GLS, United Kingdom
French National Authority for Health (HAS), Paris, 75, France
Florent Guelfucci, MSc, PhD
Syneos Health, RENNES, 35, France
Noemi Kreif, MA, MS, PhD
Imke Mayer Mayer, PhD
Owkin, PARIS, France
Juan García Burgos, MD
TLV, Tandvårds- och läkemedelsförmånsverket / Dental and Pharmaceutical Benefits Agency, Stockholm, Sweden
Francois Houyez, Patient Advocate
EURORDIS - Rare Disease Europe, Paris, France
OPEN Health Communications, London, LON, United Kingdom
Renske Ten Ham, PhD, PharmD, MSc
University Medical Center Utrecht, Utrecht, Utrecht, Netherlands
David Thomson, BSc(Hons), MSc
National Institute for Health and Care Excellence, Manchester, United Kingdom
Julie Roiz, MSc
Lumanity, London, DBY, United Kingdom
Maiwenn Al, PhD
Erasmus School of Health Policy & Management, Rotterdam, DBY, Netherlands
Miranda Cooper, MSc
AstraZeneca, Sheffield, DBY, United Kingdom
Tracy Westley, MScPH
Lumanity, Dundas, NB, Canada
Nan Qiao, PhD, MPH, MSc, MBBS
Merck, Lansdale, PA, USA
Andrew Briggs, DPhil, MSc, MSc, BA
London School of Hygiene and Tropical Medicine, London, London, United Kingdom
Anke-Peggy Holtorf, PhD, MBA
PCIG @ HTAi.org, Basel, Basel-Stadt, Switzerland
OVERVIEW: Timeframe 45 min: moderator and panelists introducing their perspective in a motivational speech (5 minutes each), 15 minutes discussion on selected topics and 10 minutes on questions from auditorium
Stephanie Rosenfeld, Dr. med.
Bayer AG, Berlin, Germany
Michael Berntgen, MSc
Inka Heikkinen, MSc, MBA
Lundbeck Pharma, Copenhagen, Copenhagen, Denmark
Zorginstituut Nederland (ZIN), Diemen, Netherlands
ISSUE: JCA will roll out in 2025, starting with all new oncology products. This will expand to include products with orphan designation in 2028. Assessment of rare diseases will face additional challenges compared with non-orphan products in terms of clinical trial design, low patient numbers, heterogenous populations, and differing standards of care across the 27 European Union member states. It is important that companies start preparing now to ensure studies are designed appropriately and to understand the PICOs (population, intervention, comparator, and outcomes) that will apply. It may be appropriate to use pragmatic approaches to reduce the research questions to be appraised and innovative methodology to consider the available evidence, both from clinical trials and real-world data. Understanding the potential options to efficiently consider all relevant populations and to ensure that a voice is given to patients across these populations within rare diseases will be vital. All stakeholders will need to collaborate to balance challenges due to the constraints of the time and data available with the needs of patients.
OVERVIEW: This panel discussion will convene experts in rare diseases, patient advocacy, and statistics to share their insights and experiences regarding JCA requirements. Caroline Ling will moderate the session, introducing the topic and the panellists. Abigail Stevenson will explore the considerations and potential challenges associated with evidence generation across all 27 European Union member states for rare diseases. Emma Hawe will consider efficient approaches to evidence generation from the perspective of literature reviews and indirect comparisons where multiple PICOs exist. The patient advocate will articulate the significance of giving a voice to specific populations in rare diseases, emphasizing the urgency of access to new treatments and importance of patient-relevant outcomes. The panel presentations will span 45 minutes, followed by a 15-minute question-and-answer session, allowing the audience to engage with the panellists.
Caroline Ling, PhD
Emma Hawe, M.Sc.
Abigail Stevenson, DPhil
Chiesi, Manchester, LAN, United Kingdom
Tbc Tbc, TBC
TBC, TBC, LON, United Kingdom
PURPOSE: Equity continues to be a significant area of HEOR research. This workshop will describe what methods are available to address equity in health technology assessment (HTA) and discuss how and to what extent HTA agencies should consider equity. It will also explore the opportunities and challenges of incorporating health equity in HTA.
DESCRIPTION: Although there is an ever-growing body of research on equity, including methods such as distributional cost-effectiveness analysis (DCEA), the incorporation of equity in HTA is inconsistent and is not well defined. How HTA agencies consider equity is a shifting landscape, and is one that will likely evolve in the coming years.
In this workshop, Ms. Cresswell will introduce the topic of equity in HTA, including how NICE was created to end the ‘post-code lottery’ and its work to date around equity (10 minutes). Dr. Jansen (methodologist perspective) will introduce the concept of DCEA as a quantitative method to quantify the health equity impact of a technology using an illustrative model-based example, and will highlight potential implementation challenges (10 minutes). Dr. Whittington (HTA perspective) will discuss the practical and ethical challenges of conducting a DCEA within a health technology assessment, and will describe other non-DCEA opportunities to incorporate equity within HTA (10 minutes). Dr. Gsteiger (industry perspective) will explore the application of equity considerations and DCEA in clinical development programs, and will discuss current challenges (including collecting the data needed for DCEA), opportunities, and prerequisites for a more widespread use in practice (10 minutes). The workshop will allocate 20 minutes for an interactive audience discussion, including question and answer sessions. Audience polling will be employed throughout to encourage active participation and engagement. The workshop will culminate by asking the audience how they think equity should be considered in HTA.
Sandro Gsteiger, PhD
F. Hoffmann-La Roche Ltd, Basel, BS, Switzerland
PURPOSE: ‘Value measurement: Assessing Value and QALY Alternatives’ remains a key trend in health economics and outcomes research. The goal of this workshop is to raise awareness of the EQ-HWB (EQ Health and Wellbeing instrument). The workshop will also explore the opportunities and strengths perceived by the speakers and audience on using the EQ-HWB-S, both from the perspective of health technology assessment (HTA) agencies and industry.
DESCRIPTION: The EQ-HWB has been designed as a standardised measure of broader aspects of quality of life related to health, social care and informal carers enabling assessment of interventions that are delivered in health, including public health, and social care. A short version, the EQ-HWB-S, can be used to generate QALYs for HTA. The instrument has experimental status which has enabled collaborators and researchers to assess its performance in diverse populations. ZIN, the HTA agency for the Netherlands, has named it as a potential measure for use when evaluating interventions in chronic conditions. Understanding the views of those who will use the tool and appraise it are important to consider.
Clara Mukuria, MA, MSc, PhD
Jeremy Dietz, MSc
National Institute for Health and Care Excelllence, Bristol, BST, United Kingdom
Reva Efe, MD, MSc
Zorginstituut Nederland, Diemen, NH, Netherlands
Andrew Lloyd, DPhil
Acaster Lloyd, London, London, United Kingdom
9:15AM - 9:30AM
OBJECTIVES: Antimicrobial resistance (AMR) is increasingly becoming a global public health priority. Payers have traditionally relied on cost-effectiveness analysis (CEA) to value novel interventions, but standard CEA methods often fail to capture key value elements for novel antimicrobials. Some of these value elements were proposed in the 2021 AMR-specific STEDI (spectrum, transmission, enablement, diversity and insurance) framework.
METHODS: We searched for economic studies published between January 2021 and April 2024 which included AMR, where the intervention was a pharmaceutical intervention or a related test. Public health, policy or stewardship interventions were excluded. Modelling methods and approaches were extracted and assessed against each value element within the STEDI framework.
RESULTS: 6,692 studies were screened and 19 studies included (seven disease transmission models, five decision tree models, two Markov models, two agent-based models, two economic evaluations alongside trials and one theoretical economic model).
CONCLUSIONS: Our review suggests that awareness of the STEDI framework and its value elements remains low. Few authors have attempted to include multiple value elements within any economic modelling structure. Despite some attempts to include multiple STEDI value elements, there remains a considerable gap for an economic modelling approach which can capture all five value domains within the STEDI framework.
9:00AM - 9:15AM
OBJECTIVES: Climate breakdown is affecting human health globally. The National Health Service (NHS) generates 26 million tonnes of CO2e per annum equivalent to the size of Croatia’s annual emissions. Healthcare suppliers possess a sizeable opportunity to support health technology environmental sustainability (HTES) underpinned by leaner pathways and resource optimization. HTA is developing approaches to include environmental sustainability (ES). Reporting a broad range of environmental outcomes is important, but there is a lack of published guidance. This study aims to develop recommendations for decision makers on how broader environmental criteria should be included in HTA, based on the technical validation of an environmental model of Aurora EV-ICD by Medtronic.
METHODS: The Medtronic environmental model estimates the impact of the device on CO2e, water usage and waste volumes over a 10-year time horizon. A multi-step process was used to internally validate the model, using a specifically designed checklist for HTES models being reported as an information conduit. Recommendations were provided to support appropriate reporting of the data to the NHS.
RESULTS: The model structure was deemed appropriate and suitable for submission to the NHS. Recommendations for reporting ES to the NHS include: 1) using and referencing multiple environmental management guidelines for reporting CO2e to ensure transparency and reproducibility; 2) applying the same principles to quantify other environmental outcomes that will enable a more holistic evaluation of unintended consequences, including human health impact, resource use and biodiversity loss; 3) using life cycle assessment software (such as OpenLCA) to overcome data challenges and to facilitate reporting appropriate and comprehensive environmental endpoint categories.
CONCLUSIONS: This multi-perspective collaboration between industry and researchers supports ES in HTA framework development. These recommendations can be used by decision makers to aid the inclusion of environmental models that supports reporting a broader range of environmental outcomes over a long-term time horizon.
8:45AM - 9:00AM
OBJECTIVES: An MCED test has been projected to be cost-effective in the population aged ≥50 years. Prognostic factors such as family history, comorbidities, and lifestyle can define a range of subpopulations with varying cancer incidence, which each cover a subset of cancer burden. This study evaluates how cost-effectiveness relates to cancer risk in representative subpopulations of varying prevalence.
METHODS: A Markov model compared the outcomes of annual MCED testing plus standard of care (SoC) versus SoC alone in US adults aged 50 to 79 years, tailored for various high-risk groups. Analyses were conducted in the overall population aged ≥50 years and several subpopulations: first degree family history (1.38 incidence rate ratio vs general population, 55% prevalence), obesity (1.14, 38.7%), diabetes (1.12, 24%), current smoking (1.57, 16.9%), cancer survivor (1.69, 11%), heavy alcohol use (1.26, 9.4%), solid organ transplant recipients (SOT) (2.9, 2%), hereditary cancer syndromes (HCS) (1.86, 2.2%). Subpopulations were defined by cancer incidence and excess competing mortality. Survival, costs, and quality of life were calculated over a lifetime horizon.
RESULTS: MCED testing demonstrated lower incremental cost-effectiveness ratios (ICERs) for subpopulations with elevated cancer risk versus the overall population aged ≥50 years ($66,043/QALY). ICERs were: diabetes ($66,010), obesity ($60,245), family history ($40,155), cancer survivor ($36,774), heavy alcohol use ($35,515), current smokers ($26,850), SOT ($18,013), HCS ($544). Several high-prevalence subpopulations had ICERs <$50,000/QALY, while the lowest ICERs were predicted in smaller subpopulations.
CONCLUSIONS: While MCED testing has been projected to be cost-effective for the general population aged ≥50 years, prioritizing higher cancer risk groups allows targeting more favorable ICERs at the cost of addressing a smaller fraction of the total population burden.
9:30AM - 9:45AM
OBJECTIVES: Inequalities in breast cancer exist by deprivation (mortality) and ethnicity (incidence and mortality). Recent NICE appraisals have highlighted inequalities in breast cancer according to ethnicity but have not quantified these. Distributional cost-effectiveness analysis (DCEA) evaluates the cost-effectiveness of health interventions within different demographic groups. A key data requirement of DCEA is quality-adjusted life expectancy (QALE) by subgroup; these data are currently unavailable by ethnicity in the UK. This study therefore aimed to conduct an aggregate DCEA in breast cancer with Index of Multiple Deprivation (IMD) as the domain of equity.
METHODS: Published data enabled the development of an aggregate DCEA, requiring stratified incidence data only, with IMD as equity subgroup. Population subgroups, baseline health distributions and opportunity costs were defined by IMD quintile. Representative incremental costs and quality-adjusted life years (QALYs) for a hypothetical treatment in breast cancer were used. The Atkinson social welfare function quantified trade-offs between health maximization and equity.
RESULTS: At a £30,000/QALY threshold and an Atkinson parameter of 10.95, the incremental population net health benefit was 15.8 QALYs, and the incremental Equal Distribution Equivalent Health (EDEH) was 4.2, suggesting an improvement in population health, but a worsening of health inequalities according to IMD.
CONCLUSIONS: This study highlights challenges in quantifying how new technologies may impact health inequalities in the UK. There are known inequalities according to ethnicity in breast cancer, however, we were unable to assess that inequality due to data insufficiency, and a DCEA in breast cancer found potentially worsening inequalities by IMD. There is a need for better data across all domains of equity, particularly those with published inequalities in unmet need such as ethnicity. This would enable a better assessment of treatment impact on inequalities to inform healthcare investment. A full DCEA, considering inequalities in both incidence and mortality, would provide a more comprehensive assessment.
The moderator will introduce the main points in the NICE AI statement and present some background HEOR research that may have mobilized the agency to publish such statement. The other panelists will present specific opportunities and challenges in translating the NICE position on AI during conducting systematic literature reviews, comparative clinical and cost-effectiveness analysis and using RWE in data analytics. Finally, the moderator will close by presenting a roadmap (checklist) to be used when AI tools are considered in NICE submissions and will open the floor for discussion with the audience.
Cytel
Cytel, Vancouver, BC, Canada
Thomas Macmillan, MSc
Sophie van Beekhuizen, MSc, MPhil
OVERVIEW: Neil Grubert (moderator) will begin with an overview of the key developments in cross-border collaboration in recent years. He will identify the key features of the leading regional, pan-European and transcontinental initiatives.
Dr Roisin Adams will reflect on the challenges and opportunities for regional partnerships, such as the Beneluxa Initiative and the International Horizon Scanning Initiative, and consider how they may evolve. She will also examine how the Heads of HTA Agencies will support voluntary cooperation on HTA.
Johan Potén will share insights on how the Nordic region has become a pioneer in cross-border collaboration, bringing together HTA and procurement to accelerate access to promising new treatments.
Dr Meindert Boysen will discuss the role the Novel Medicines Platform could play in improving access to innovative health technologies across Europe and beyond, and what other regions might learn. He will also consider how mature HTA agencies from different continents are working together in alliances such as the AUS-CAN-NZ-UK Collaboration Arrangement, and how they are helping less experienced countries to develop HTA systems.
Q&A
Neil Grubert, MA
Independent, Basildon, United Kingdom
Thilo Schaufler, Ph.D.
CSL Vifor, Glattbrugg, ZH, Switzerland
Sue Harnan, MSc
The University of Sheffield, SHEFFIELD, United Kingdom
Helene Vioix, PharmD, MSc
Evidence and Value Development, Merck Healthcare KGaA, Darmstadt, Germany
Vicki Young, PhD
Cytel Statistical Services Ltd, London, LON, United Kingdom
ISSUE: In our increasingly connected world, patients find information and support from their peers online, via social media and forums. These platforms provide a rich tapestry of lived patient experiences, capturing potentially valuable insights on topics such as disease burden, quality of life, and treatment outcomes. Until recently, the information within social media platforms remained under-utilised; however, capturing these data via social media listening (SML) has garnered attention as an assessable, low-burden, real-time source of real-world data (RWD). Historically, barriers to the wider adoption of SML have included lack of acceptance/guidance from key stakeholders, challenges with handling large unstructured data, and issues regarding bias, user privacy and content reliability. However, SML is now regaining attention with the advent of new evidence standard frameworks for digital health technologies and a growing interest in leveraging artificial intelligence to analyse big data, making it timely to re-examine its capacity in enhancing patient-centric real-world evidence. This session examines the promises and limitations of SML as a medium of RWD, sitting at the crossroads of many “hot topics” in the pharmaceutical industry.
OVERVIEW: Siddharth will open with a 5-minute balanced overview of SML as a medium of RWD. Next, Jackie will discuss the mechanics, value, and impact of SML and the use of AI to bring efficiencies to SML analysis. Following this, Mary Jo will examine challenges encountered in the use of AI from a clinical development/operations perspective and illustrate use cases of AI/SML informing adverse event reporting. Finally, Jeanette will provide a decision-maker’s perspective on the evidence standards that SML would need to meet to be useful for health technology assessment and acceptable use cases. Speakers are each allotted 10-minutes, followed by 20-minutes discussion time with audience participation encouraged throughout. Stakeholders from academia, industry and patient advisory groups would benefit from attending.
Siddharth Ramanan, PhD
Costello Medical, Cambridge, CAM, United Kingdom
Jackie Cuyvers, MSc
Convosphere, London, London, United Kingdom
Mary Jo Lamberti, PhD
Tufts University, Boston, MA, USA
ISSUE: The EC proposed pharmaceutical directive will require developers to be transparent regarding any direct financial support received from public bodies for R&D of medicines. This issue panel explores the implications and reactions of academia, industry, and payers. How should companies report the public funding received? How can payers use this information in HTA and price negotiations? How does it affect incentives to finance and conduct R&D?
Jaime Espin, PhD
Andalusian School of Public Health, Granada, GR, Spain
David Mark Epstein, Phd
University of Granada, Granada, Spain
Carole Longson, MBE
Carole Longson Consultant, Manchester, United Kingdom
Mark Sculpher, PhD
University of York, York, United Kingdom
Peter Neumann, ScD
Tufts Medical Center, Boston, MA, USA
Dan Ollendorf, PhD
Institute for Clinical and Economic Review, Boston, MA, USA
Beth Woods, BA, MSc
Nicholas Adlard, MA MSc MBA
Novartis Pharmaceuticals UK Ltd, Busserach, SO, Switzerland
Stefan Lange, MD
Dierks+Company, Berlin, Germany
PURPOSE: To test the feasibility of accounting for carbon emissions in HTA and go beyond theoretical considerations by showing and discussing worked examples for carbon footprint (CF) assessment inspired by methods used in clinical trial conduct.
DESCRIPTION: Previous discussions at ISPOR addressed the normative aspect of incorporating environmental impacts in HTA, but they stopped short of practical consideration of how this could be implemented. HEOR is not the only field grappling with methodological and data availability challenges related to assessing greenhouse gas (GHG) emissions, and we can learn from initiatives in other parts of the industry, such as clinical trial conduct. This session will focus on practical examples of methods to incorporation of carbon footprint assessment into HTA.
Ruth Chapman, PhD
Evidera Ltd, London, LON, United Kingdom
Michael Cohen, MSc, MBA, SEA
PPD, Ft Lauderdale, FL, USA
Kinga Marczell, PhD
Evidera, London, United Kingdom
Harlan Campbell, PhD
University of British Columbia, Vancouver, BC, Canada
Gihan Elsisi, PhD
The American University in Cairo, cairo, C, Egypt
10:00AM - 10:15AM
OBJECTIVES: Although the treatment of Disease-Related Malnutrition (DRM) reduces rehospitalizations by 27.5% and the average length of stay (ALOS) by 26.1%, the establishment of nutritional treatment at home in Spain is limited by an outdated regulatory framework. The aim of this research is to estimate the financial burden that a possible extension of DRM treatment at home would entail after hospital discharge.
METHODS: A group of chronic diseases/clinical conditions that nutrition experts propose to include in the financing of Home Enteral Nutrition (HEN) was analyzed, when they are associated with severe DRM: severe COPD, New York Heart Association Class III-IV Heart Failure, Child-Pugh C Chronic Liver Disease, Chronic Kidney Disease stage IV, and Pressure ulcers stage III-IV. Target population was estimated from a systematic literature review, prioritizing studies on the Spanish population, and data from official sources. The cost of HEN (Oral Nutritional Supplementation: 400 mL/day for 12 weeks) and hospitalizations was included in the calculation of the net cost per patient. A dissemination rate of 24% (expert opinion) and a time horizon of one year were assumed, from the perspective of the National Health System (NHS).
RESULTS: Per year, 14,096 patients are discharged with these chronic diseases/conditions and severe DRM from Spanish public hospitals. The average cost of the HEN is €742.40/patient, and the cost of hospitalization is €900.56/day. The reduction in rehospitalizations and ALOS in those receiving HEN allows us to estimate a net cost per patient treated with HEN of -€3,240.69. The financing of 24% of these patients would generate savings of almost eleven million euros for the NHS.
CONCLUSIONS: The financing of the HEN in these diseases would have an impact on significant savings for the NHS, even without considering other savings due to a reduction in non-hospital care resources (other medicines, home care).
OBJECTIVES: Changes in Methods and Processes (M&P) of Health Technology Assessment (HTA) agencies can affect time and degree of patient access to treatments. Our study investigates HTA reforms and explores interdependencies and proactivity in implementing M&P reforms by 14 HTA agencies in Europe, Asia-Pacific, and North America. The study explores how the agencies' positions on modifiers, real-world evidence (RWE), patient involvement, surrogate endpoints and discounting have evolved.
METHODS: We conducted a targeted literature review on M&P guidelines and subsequent changes implemented by 14 HTA agencies from 2010 to 2023. We supplemented and validated initial findings with 29 semi-structured interviews with country-specific experts. We used analytical tools to create interdependency networks and a framework to group HTA agencies based on proactivity and influence.
RESULTS: We classified PBAC(Australia), CADTH(Canada), NICE(England), IQWiG(Germany), and ZIN(the Netherlands) as catalysts of HTA reforms and internationally influential; HAS(France), TLV(Sweden), and KCE(Belgium) as traditionalists; DMC(Denmark), AIFA(Italy), INFARMED(Portugal), ACE(Singapore), AEMPS(Spain), and CDE(Taiwan) as observers.
CONCLUSIONS: Our work provides an overview of past HTA M&P reforms internationally. While some HTA agencies have evolved and formalised their positions on certain aspects, others lack an explicit stance. International collaborations represent a valuable route to accelerate changes by encouraging consistency and providing leadership. To be fully successful, collaborations should ensure wide involvement of stakeholders at an early stage.
OBJECTIVES: The National Institute for Health and Care Excellence (NICE) launched its advice service, NICE Scientific Advice, in 2009. A previous study reported that medicinal products that received scientific advice had a reduced time for NICE appraisal than those that did not. To identify the recent impact of scientific advice on time for NICE appraisal, we conducted a study on medicinal products that sought scientific advice from 2020 to 2023.
METHODS: This study examined NICE Technology Appraisal (TA) and Highly Specialised Technologies (HST) guidance published from June 2020 to May 2023. It identified technologies that received scientific advice from the NICE Advice database and matched the intended conditions to the published guidance. The study compared the time from marketing authorisation to guidance publication for technologies that received scientific advice verses those that did not.
RESULTS: During the study period, 277 TA and HST guidance documents were published, of which 273 were analysed. Four were excluded because 2 were non-pharmaceutical and 2 were withdrawn.
CONCLUSIONS: The results of this study support previous findings, showing the benefits of NICE scientific advice for medicinal technologies. Products that received scientific advice had a shorter time from marketing authorisation to the publication of the NICE TA or HST guidance.
OBJECTIVES: The use of RWE as a component of Health Technology Assessment (HTA) submission has seen a marked increase in recent years. This study aims to understand this increasing trend of RWE submission to NICE and CADTH in the last 3 years as well as the impact of recent RWE guidance published by these HTA agencies.
METHODS: A search was conducted on NICE and CADTH websites for HTAs published between January 2021 and December 2023. After excluding non-relevant HTAs, selected appraisals with RWE were further analysed for study type, data sources, and their type of use in the submission e.g. utility cost, and efficacy.
RESULTS: A total of 539 and 256 appraisals, were identified on NICE and CADTH websites, respectively. Among included appraisals, around 60% in the UK and 37% in Canada included RWE. Most RWE studies in both countries were observational, primarily utilizing retrospective cohorts, and the predominant sources of data were patient & disease registries, or electronic health records. Overall, the most common use of RWE included in the submissions was related to efficacy, patient characteristics and healthcare resource utilization. Of all the CADTH and NICE appraisals incorporating RWE, around 50% were in oncology. A trend of increasing RWE submissions was observed in both countries, rising from 51% in 2021 to 66% in 2023 in the UK and from 24% to 47% in Canada.
CONCLUSIONS: NICE and CADTH lean towards increased importance and thoroughness given to RWE in the context of the HTA processes as shown by this analysis and recent guidelines. Nonetheless, HTA in general still lacks clarity about which type of RWE study and data sources can be used and when. There is great potential to drive mutual understanding of RWE’s impact further, establish acceptance thresholds, and ensure transparency in decision-making related to RWE
Iñaki Imaz-Iglesia, MD, PhD, MPH
Institute of Health “Carlos III” (AETS-ISCIII), Madrid, Spain
OBJECTIVES: To estimate the extent to which Canadians are averse to health inequalities, a critical component for equity-informative economic evaluations but lacking in the Canadian context.
METHODS: We conducted three experiments among representative samples of adult Canadians to elicit value judgements about reducing socioeconomic-related health inequality (i.e. the gap in healthy life expectancy between the highest and lowest household income quintiles) vs. improving overall population health. Each experiment compared two programs: (i) universal and tailored vaccination; (ii) non-specific prevention programs (universal prevention vs. tailored prevention interventions); (iii) generic health care programs (program A vs. program B). Tailored programs and program B had a more equitable distribution of additional life years, while universal programs and program A were more efficient. We used benefit trade-off analysis to estimate the Atkinson health inequality aversion index.
RESULTS: We recruited 3,000 adult Canadians (1,000/experiment). Preferences for the vaccination, prevention, and generic interventions were distributed as follows: minimizing inequalities (i.e., egalitarians; Atkinson Index undefined): 54%, 55%, and 57%, respectively; maximizing the health of the population with the highest income (i.e., pro rich; Atkinson Index<0): 31%, 22%, and 16% respectively; willingness to trade some health to reduce inequalities (i.e., weighted prioritarians; Atkinson Index>0): 13%, 19%, and 22% respectively; improving the health of the individuals with the lowest income (i.e., maximins; Atkinson Index=∞): 0%, 1%, and 3%, respectively; and maximizing total health (i.e., health maximizers; Atkinson Index=0): 2%, 3%, and 2%, respectively. The median response reflected a preference for minimizing health inequalities across the three experiments. A stronger aversion to health inequality was observed among females, younger respondents (18-40 years old), and respondents with lower income (<$50,000 household income per year).
CONCLUSIONS: Our findings suggest a strong aversion to health inequality among Canadians with over half of respondents consistently willing to minimize health inequalities regardless of the cost to efficiency.
OBJECTIVES: Health equity (HE) ensures equitable healthcare access for all, regardless of socio-economic status, identity, or background, and is a key WHO global health priority. Asylum seekers, refugees, and recent migrants in the European Union (EU) are often excluded from HE evaluations due to their temporary residence status, though many settle locally. This study examines inclusion of asylum seekers, refugees, and recent migrants hosted by the EU in data collection used for HE evaluation.
METHODS: Measuring health disparities among population subgroups is a standard approach for assessing HE. Characteristics such as socio-economic status and ethnicity are used to classify populations into subgroups, while unmet health needs is a metric measuring access to healthcare across defined subgroups. Data is often collected through household surveys that track expenditures on goods and services (including healthcare).
RESULTS: Despite hosting 1.1 million asylum seekers and 7.1 million refugees in the EU in 2023, all surveys excluded these populations in national statistics on expenditures and healthcare services consumption. Primary reason for exclusion was an anticipated non-response rate due to linguistic barriers and misunderstanding of survey objectives. Only migrants with longer-term residence were included whose unmet health needs in Germany, Greece, Spain, and France in 2019 was 11.6%, while all residents in these countries experienced unmet needs ranging from 0.2% (Spain) to 8.1% (Greece).
CONCLUSIONS: Exclusion of vulnerable populations when measuring access to health may result in underestimation of health equity metrics which may be misleading for policy makers.
OBJECTIVES: Health investments impact labor supply by affecting workforce health and capabilities. We introduce a novel methodology combining value chain effects and health economics to show how health investments drive economic development. Using the concept of human capital— abilities, skills, knowledge, and motivation—we estimate the monetary value of the socioeconomic (SoC) burden of disease. It reflects how disease reduces labor supply by impairing individuals' capital utilization. The method is applied to the Brazilian case for cardiovascular, neoplasms, ischemic heart disease, lower respiratory infections, breast cancer, type 2 diabetes, and migraine.
METHODS: We assumed that years lost due to disability or mortality are non-productive, where Years Lived with Disability (YLDs) captures presenteeism and absenteeism, and Years of Life Lost (YLLs) premature mortality. Two approaches are considered: the Human Capital Approach (HCA), which assumes an irreplaceable loss of productivity upon premature death, and the Friction Cost Approach (FCA), which assumes that within a year, the deceased's tasks are redistributed, absorbed, or adapted within the production process. Moreover, using Input-Output matrices, we estimate the direct economic effects and calculate spillover effects in adjacent economic sectors and induced effects along value chains. Additionally, we addressed both paid and unpaid work.
RESULTS: In 2022, Brazil's GDP loss due to diseases was 4.1% ($77.1 billion) under the FCA and 7.1% ($135.9 billion) under the HCA. Migraines and type 2 diabetes were the top contributors under the FCA. Under the HCA, cardiovascular disease and migraines have the highest burden.
CONCLUSIONS: Decreases in labor supply not only hinder economic growth but also exacerbate inequalities and adversely affect vulnerable populations, as seen in Brazil. Key questions arise: What is the socioeconomic value of investing in health? Which healthcare strategies promote economic development? By measuring the SoC burden, we bring light to these questions and help policymakers prioritize budget allocations.
OBJECTIVES: Reducing health inequalities is a policy priority for the National Health Service (NHS) in England. Breast cancer is the most common cancer in women in the UK; despite premature mortality rates declining, inequalities in care remain. The aim of this study was to model the potential impact of levelling up inequalities in the breast cancer care pathway for women in England.
METHODS: A targeted literature review and analysis of publicly available NHS data sources (2016-2022) were used to describe variation in the breast cancer pathway for women in England by socioeconomic deprivation, geography, and ethnicity. The potential additional life years (PALYs) saved if variations in stage at diagnosis were addressed was modelled using a parametric survival model to extrapolate NHS 5-year survival data.
RESULTS: Women living in the most deprived communities, and black women, experience significant inequalities across the pathway. Diagnosis via screening across socioeconomic groups and ethnicity was lowest for black women (23%) and women living in the most deprived communities (29%) compared to white women (34%) and women in the least deprived communities (36%). 22% of black women were diagnosed with late-stage cancer compared to 14% of white women. Women in more deprived communities had the highest incidence of late-stage cancer compared to women in less deprived communities. Levelling up inequalities in stage at diagnosis could result in black women in England gaining 6 additional months of life, and women in the most socioeconomically deprived communities gaining an increase of 3 months.
CONCLUSIONS: Addressing inequalities in the diagnosis and treatment pathway could improve survival outcomes for women with breast cancer in England. There are opportunities for health systems and stakeholders to work collaboratively to uncover the root causes of breast cancer inequalities to identify solutions to tackle these.
We, regulators, HTA agencies, payers and pharmaceutical industry, are all working closely with patients to ensure that innovative drugs can benefit patients as early as possible. This collaboration is guided by the ambition to enable and use the generation of reliable and valid evidence that will address and answer different questions, e.g. for benefit/risk assessment, for relative effectiveness assessment... We are relying on the totality of evidence generated, either from randomized controlled trials, or from other designs such as studies using real-world data. Even if differences in scope may exist, there are overlapping commonalities in what constitutes meaningful evidence generation. We also must deal with (remaining) uncertainties at different stages of decision making. How can we limit these uncertainties, and ensure that at the end of the overall approval process, all stakeholders get the needed evidence to take an informed decision and ensure an innovative drug reach the patients if appropriate?
Nikos Dedes, BA
Greek Patients' Association, Athens, Attika, Greece
Almath Spooner, .
Abbvie, Dublin, Ireland