Event

Innovative causal inference methods are needed for the design and analysis of big real-world observational data and pragmatic trials used for outcomes research and health technology assessment. This course will introduce the principles of causation in comparative effectiveness research, the use of causal diagrams (directed acyclic graphs; DAGs) and focus on causal inference methods for time-independent confounding (multivariate regression, propensity scores) and time-dependent confounding (g-formula, marginal structural models with inverse probability of treatment weighting, and structural nested models with g-estimation). The “target trial” concept and a counterfactual approach with “cloning – censoring – weighting” will be used to apply causal methods to big real-world datasets with case examples from oncology, cardiovascular disease, HIV, nutrition and obstetrics. The course consists of lectures, practical training exercises, case examples drawn from the published literature which will be used to comprehend the use of causal inference in health technology assessment bodies (eg, NICE , IQWiG), and an interactive discussion with Q&A. The intended audience includes all stakeholders in medical and public health decision making and healthcare, and researchers from all substance matter fields, statisticians, epidemiologists, outcome researchers, health economists, modelers and health policy decision makers interested either in methods of causal analysis or causal interpretation of results based on the underlying method. Course material includes all session handouts, exercises with solutions, a comprehensive background reading library, and software recommendations.

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

Research Fellow, Global Statistical Sciences

Eli Lilly and Company

Indianapolis, IN, USA

Senior Scientist, Program Causal Inference

UMIT - University for Health Sciences

Medical Informatics and Technology

Innsbruck, Austria