I’ve previously written about methods from causal inference (G-formula and G-estimation) that can be used to exploit data observed after patients experience intercurrent events (ICEs) to improve the precision (and hence statistical power) of estimates of treatment effects in clinical trials. Thanks to useful comments from reviewers, in our revised paper (open access version) on the topic, we explore the robustness of these methods, and those that do not make use of such data, to misspecification in model assumptions.
g-formula
Estimating hypothetical estimands with causal inference and missing data estimators in a diabetes trial
We (Camila Olarte Parra (LSHTM), Rhian Daniel (Cardiff), myself, and David Wright (AstraZeneca)) recently put on arXiv a new paper which explores the use of estimators from both the causal inference and missing data literatures for estimating a so-called hypothetical estimand in a previously conducted clinical trial in diabetes.
G-formula for causal inference via multiple imputation
G-formula (sometimes known as G-computation) is an approach for estimating the causal effects of treatments or exposures which can vary over time and which are subject to time-varying confounding. It is one of the so called G-methods developed by Jamie Robins and co-workers. For a nice overview of these, I recommend this open access paper by Naimi et al 2017, and for more details, the What If book by Hernán and Robins. In this post, I’ll describe some recent work with Camila Olarte Parra and Rhian Daniel in which we have explored the use of multiple imputation methods and software as a route to implementing G-formula estimators.