Estimators for handling COVID-19-related intercurrent events with a hypothetical strategy

The COVID-19 pandemic has affected clinical trials across disease areas, raising the questions how interpretable results can be obtained from impacted studies. Applying the estimands framework, analyses may seek to estimate the treatment effect in the hypothetical absence of such impact. However, no established estimators exist. This simulation study, based on an ongoing clinical trial in patients with Tourette syndrome, compares the performance of candidate estimators for estimands including either a continuous or binary variable and applying a hypothetical strategy for COVID-19-related intercurrent events (IE). The performance is investigated in a wide range of scenarios, under the null and the alternative hypotheses, including different modeling assumptions for the effect of the IE and proportions of affected patients ranging from 10% to 80%. Bias and type I error inflation were minimal or absent for most estimators under most scenarios, with only multiple imputation- and weighting-based methods displaying a type I error inflation in some scenarios. Of more concern, all methods that discarded post-IE data displayed a sharp decrease of power proportional to the proportion of affected patients, corresponding to both a reduced precision of estimation and larger confidence intervals. The simulation study shows that de-mediation via g-estimation is a promising approach. Besides showing the best performance in our simulation study, these approaches allow to estimate the effect of the IE on the outcome and cross-compare between different studies affected by similar IEs. Importantly, the results can be extrapolated to IEs not related to COVID-19 that follow a similar causal structure.