November 12: Causal Inference and Causal Diagrams in Big, Real-World Observational Data and Pragmatic Trials   - In Person at ISPOR Europe 2023

November 12, 2023

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Causal Inference and Causal Diagrams in Big, Real-World Observational Data and Pragmatic Trials (in person) 

Real World Data & Information Systems
4 Hours | Course runs 1 day

This short course is offered in-person at the ISPOR Europe 2023 conference. Separate registration is required. Visit the ISPOR Europe 2023 Program page to register and learn more.

Sunday, 12 November 2023 | Course runs 1 Day
13:00-17:00 Central European Time (CET) 


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).

Registrants receive a digital course book. Copyright, Trademark and Confidentiality Policies apply.


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

Douglas E. Faries, PhD
Research Fellow, Global Statistical Sciences
Eli Lilly and Company
Indianapolis, IN, USA

Felicitas Kühne, MSc
Senior Scientist, Program Causal Inference
UMIT - University for Health Sciences
Medical Informatics and Technology
Innsbruck, Austria


Basic Schedule:

4 Hours | Course runs 1 Day

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