Advanced Epidemiologic Methods – EPI207

Instructors: James Robins and Miguel Hernán

This course provides an in depth investigation of statistical methods for drawing causal inferences from observational studies with time-varying treatments. Epidemiologic concepts such as time-varying confounding and selection bias, intermediate variables, overall effects and direct effects are formally defined within the context of a counterfactual causal model. Methods for the analysis of the causal effects of time-varying exposures in the presence of time-varying covariates that are simultaneously confounders and intermediate variables are emphasized. These methods include g-estimation of structural nested models, inverse probability weighting of marginal structural models, and the g-formula. As a practicum, students reanalyze data sets using the above methods.

Fall semester, Harvard School of Public Health