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Causal Inference and Causal Estimands from Target Trial Emulations Using Evidence from Real-World Observational Studies and Clinical Trials (in person)
LEVEL: Experienced
TRACK: Real World Data & Information Systems
LENGTH: 4 Hours | Course runs 1 day
This short course is offered in-person at the ISPOR Europe 2024 conference. Separate registration is required. Visit the ISPOR Europe 2024 Program page to register and learn more.
Sunday, 17 November 2024 | Course runs 1 Day
08:00-12:00 Central European Time (CET)
DESCRIPTION
Separate registration required.
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).
Registrants receive a digital course book. Copyright, Trademark and Confidentiality Policies apply.
FACULTY MEMBERS
Uwe Siebert, MD, MPH, MSc, ScD
Professor & Chair
UMIT - University for Health Sciences
Medical Informatics and Technology
Hall in Tirol, Austria and
Harvard Chan School of Public Health
Harvard University
Boston, MA, USA
Felicitas Kühne, MSc
Manager Outcomes Research
Health & Value Germany
Pfizer Pharma GmbH
Berlin, Germany and
Senior Scientist, Program Causal Inference
UMIT - University for Health Sciences
Medical Informatics and Technology
Innsbruck, Austria
Nicholas Latimer, MSc, PhD
Professor of Health Economics
SCHARR, University of Sheffield
Sheffield, Derbyshire, Great Britain and
Delta Hat Limited
Analyst
Nottingham, UK
4 Hours | Course runs 1 Day