Retrieved dropout imputation considering administrative study withdrawal

TL;DR

This paper proposes two novel methods for imputing missing data due to administrative study withdrawal in clinical trials, considering the impact of intercurrent events, and demonstrates their effectiveness through simulations and real data application.

Contribution

The paper introduces two new methods for handling missing data from administrative withdrawals, accounting for intercurrent events, within the retrieved dropout imputation framework.

Findings

Both methods perform well in simulations.

Methods effectively handle administrative withdrawal data.

Application to real trial data demonstrates practical utility.

Abstract

The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E9 (R1) Addendum provides a framework for defining estimands in clinical trials. Treatment policy strategy is the mostly used approach to handle intercurrent events in defining estimands. Imputing missing values for potential outcomes under the treatment policy strategy has been discussed in the literature. Missing values as a result of administrative study withdrawals (such as site closures due to business reasons, COVID-19 control measures, and geopolitical conflicts, etc.) are often imputed in the same way as other missing values occurring after intercurrent events related to safety or efficacy. Some research suggests using a hypothetical strategy to handle the treatment discontinuations due to administrative study withdrawal in defining the estimands and imputing the missing values based on completer data assuming missing at random, but this approach ignores the fact that subjects might experience other intercurrent events had they not had the administrative study withdrawal. In this article, we consider the administrative study withdrawal censors the normal real-world like intercurrent events and propose two methods for handling the corresponding missing values under the retrieved dropout imputation framework. Simulation shows the two methods perform well. We also applied the methods to actual clinical trial data evaluating an anti-diabetes treatment.