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

2026-04-04

AI Summary

This study addresses the challenge that existing clinical trial analysis methods struggle to jointly model completers, retrieved dropouts, and missing-at-random participants, often with unclear links between modeling assumptions and target estimands. The authors propose a likelihood-based unified framework that explicitly integrates data from these three participant types for the first time. By combining analysis of covariance with a probit model for treatment discontinuation, the approach clearly aligns its modeling assumptions with the hypothetical and treatment-policy strategies defined in ICH E9(R1). The method employs a maximum likelihood–based efficient estimation algorithm tailored for continuous endpoints and dropout mechanisms. Numerical studies demonstrate that the proposed approach substantially outperforms conventional imputation methods in terms of both bias and variability.

Technology Category

Reasoning under Uncertainty: Relational Probabilistic ModelsMachine Learning: Calibration & Uncertainty QuantificationSearch and Optimization: Mixed Discrete/Continuous Search

Application Category

Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphsUser Modeling, Personalization and Recommendation: Practical large-scale studies of user experienceSearch and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• treatment effect estimation
• retrieved dropouts
• estimand framework
• continuous endpoints
• clinical trials

Innovation

• estimand
• retrieved dropouts
• likelihood-based model
• treatment policy strategy
• analysis of covariance