Event

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).

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

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

Professor of Health Economics

SCHARR, University of Sheffield

Sheffield, Derbyshire, Great Britain and

Delta Hat Limited

Analyst

Nottingham, UK