Society for Medical Decision Making

Course Description and Objectives:

This course will provide an introduction to the principles of causation and causal diagrams, with focus on Directed Acyclic Graphs (DAG) and a brief introduction to methods for causal inference (“g-methods) including multivariate analysis, propensity scores, g-formula, marginal structural models with inverse probability of treatment weighting, and structural nested models with g-estimation (lecture - exercises - discussion). We will use the “target trial” concept and a counterfactual approach with “replicates” to apply causal methods and valid per-protocol analysis of sustained treatment regimens to big real world observational data and pragmatic trials with postrandomization confounding. 

The objectives of this course are to draw and interpret causal diagrams, to decide which biostatistical/epidemiological methods must be used in different situations to derive causal effect parameters, to understand causal modeling, and to know how these methods are applied in large real world data and pragmatic trials.

Published cardiovascular, oncology, HIV, nutrition and obstetrics examples will be used to: *** Adjust for non-adherence in randomized clinical trials, where both the intention-to-treat and the naïve per protocol analyses can fail to yield the true causal intervention effect; *** Assess the “fallibility of estimating direct effects” (i.e., adjusting for intermediate steps); *** Adjust for time-independent confounders (i.e., confounder affects both risk factor and disease), where standard stratification, regression analysis or propensity score methods yield valid causal effects if all confounders are measured, and *** Adjust for time-dependent confounding (i.e., the confounder simultaneously acts as an intermediate step in the causal chain between risk factor and disease), where standard regression analysis fails and causal methods such as marginal structural models or g-estimation must be used, and finally, *** Emulate randomized controlled trials and standardize/transport estimates to the trial population for validation purposes.

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