Estimation of treatment effect in clinical trials of continuous endpoints with retrieved dropouts

TL;DR

This paper introduces a likelihood-based model for continuous endpoints in clinical trials that explicitly incorporates retrieved dropouts, improving estimation of treatment effects under the treatment policy strategy.

Contribution

It presents a unified, likelihood-based approach that models all subject categories, including retrieved dropouts, within the estimand framework for clinical trial analysis.

Findings

The proposed method reduces bias compared to imputation methods.

It achieves better variability properties in treatment effect estimation.

Numerical studies confirm improved performance in bias and variance.

Abstract

The estimand framework provides guidance on handling intercurrent events, such as treatment discontinuation, in the analysis of clinical trial responses. Under ICH E9(R1), the treatment policy (TP) strategy incorporates post-discontinuation data to reflect treatment effects in real-world practice. However, many existing approaches focus primarily on imputing missing endpoint values for lost-to-follow-up subjects and do not explicitly model completers, retrieved dropouts (RDs), and lost-to-follow-up subjects within a unified framework. This may obscure the relationship between modeling assumptions and the estimand of interest when RD data are present. We propose a likelihood-based model for continuous endpoints that integrates data from all subject categories, including RDs. The approach combines an analysis of covariance formulation with a probit model for treatment discontinuation, enabling explicit formulation of treatment effects for estimands defined using the hypothetical and TP strategies. Estimation is carried out via a computationally efficient maximum likelihood procedure. Numerical studies demonstrate that the proposed method achieves improved bias and variability properties compared with commonly used imputation-based approaches.