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

TL;DR

This paper evaluates modified g-estimation methods for handling symptomatic medication in Alzheimer's trials, demonstrating improved efficiency while maintaining unbiasedness and proper error control in a simulation study.

Contribution

It introduces and assesses modifications to g-estimation techniques that leverage timing assumptions to enhance efficiency in clinical trial analysis.

Findings

Modified g-estimation methods show substantial efficiency gains.

Methods retain unbiasedness and proper type I error control.

Simulation results support their application in Alzheimer's trials.

Abstract

For handling intercurrent events in clinical trials, one of the strategies outlined in the ICH E9(R1) addendum targets the hypothetical scenario of non-occurrence of the intercurrent event. 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-IE data. As the g-estimation methods have largely developed outside of randomised clinical trials, optimisations 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 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 unbiasedness and adequate type I error control.