Causal Inference in Epidemiology: DAGs, g-Methods and Target Trial Emulation – A Tutorial for Researchers and Educators (Course to the Keynote)
Uwe Siebert (Hall, Tirol)
Whereas “traditional” methods (e.g., stratification, matching, multivariate regression, propensity score), which are appropriate for baseline confounder adjustment, are broadly taught and applied, the more general methods (g-methods), which are needed to control for time-varying confounding, are still less known and underused.
This tutorial covers innovative causal inference concepts and methods that are needed for the design and analysis of observational data and pragmatic trials with time-varying exposures or treatments.
We cover the following topics:
1. Introduction to the principles of causation in epidemiology
2. Use of causal diagrams (directed acyclic graphs, DAGs)
3. Brief intuitive illustration of the principles of g-methods: a) g-formula, b) marginal structural models with inverse probability of treatment weighting, and c) structural nested models with g-estimation
4. Application of the target trial emulation concept combined with a counterfactual approach using “replicates” for dynamic treatment regimes
5. Application of g-methods in observational studies and pragmatic trials with post-randomization confounding (treatment switching/non-adherence/2nd-line-treatment etc.)
6. Case examples from oncology, cardiovascular disease, HIV, nutrition and other disease areas, illustrating the bias when using “traditional” methods for time-varying confounding
The tutorial is an extension of the Causal Inference Keynote Session on Friday and will consist of lectures, exercises drawn from the published literature and interactive discussion. The intended audience includes researchers from all substance matter fields interested either in methods of causal design/analysis or in the mere interpretation of observational study results.