May 5: Causal Inference and Causal Diagrams in Big, Real-World Observational Data and Pragmatic Trials- In Person at ISPOR 2024

May 5, 2024

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Causal Inference and Causal Diagrams in Big, Real-World Observational Data and Pragmatic Trials (in person at ISPOR 2024)

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

This short course is offered in-person at the ISPOR 2024 conference. Separate registration is required.  Visit the ISPOR 2024 Program page to register and learn more.

Sunday, 5 May 2024 | Course runs 1 Day
1:00pm-5:00pm Eastern Standard Time (EST)


Separate registration required.

Innovative causal inference and target trial emulation methods are needed for the design and analysis of big real-world observational data and pragmatic trials. 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 “replicates” 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 will consist of lectures, case examples drawn from the published literature and interactive discussion. The intended audience includes researchers from all substance matter fields, statisticians, epidemiologists, outcome researchers, health economists and health policy decision makers interested either in methods of causal analysis or causal interpretation of results based on the underlying method.

PREREQUISITE: Students are expected to have a basic knowledge in epidemiologic studies and methods (including the concept of confounding).


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
Alma, AR, USA

Basic Schedule:

4 Hours | Course runs 1 Day

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