Comparison of g-estimation approaches for handling symptomatic medication at multiple timepoints in Alzheimer's Disease with a hypothetical strategy

For handling intercurrent events in clinical trials, one of the strategies outlined in the ICH E9(R1) addendum targets a hypothetical scenario where an intercurrent event would not occur. While this strategy is often implemented by setting data after the intercurrent event to 'missing' even if they have been collected, g-estimation allows for a more efficient estimation by using the information contained in post intercurrent event data. As the g-estimation methods have largely developed outside of randomized clinical trials, optimization for the application in clinical trials are possible. In this work, we describe and investigate the performance of modifications to the established g-estimation methods, leveraging the assumption that some intercurrent events are expected to have the same impact on the outcome regardless of the timing of their occurrence. In a simulation study in Alzheimer's disease, the modifications show a substantial efficiency advantage for the estimation of an estimand that applies the hypothetical strategy to the use of symptomatic treatment while retaining approximate unbiasedness and adequate type I error control.