Effect Modification and Collapsibility in Evaluations of Public Health Interventions

The American Journal of Public Health series "Evaluating Public Health Interventions" offers excellent practical guidance to researchers in public health. In the 8th part of the series, a valuable introduction to effect estimation of time-invariant public health interventions was given.[1] The authors of this article suggested that in terms of bias and efficiency there is no advantage of using modern causal inference methods over classical multivariable regression modeling.[1] However, this statement is not always true. Most importantly, both “effect modification” and “collapsibility” are important concepts when assessing the validity of using regression for causal effect estimation. We run Monte Carlo simulations and demonstrate these concepts. This repository contains the code and files used to run the simulations promoting open source reproducible science.

Effect modification and collapsibility when estimating the effect of public health interventions

Effect modification and collapsibility when estimating the effect of public health interventions