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.