Evolving Longitudinal Patient Histories and Re-enrollment in Master Protocol Trials

2026-04-27

AI Summary

This study addresses the challenges posed by repeated enrollment of participants in master protocol trials, where individuals may receive multiple therapies, leading to ambiguity in treatment effect definitions and complications in statistical inference. The authors propose a novel estimator of sequential treatment effects based on contemporaneous eligible populations and introduce an aggregated efficacy metric to synthesize outcomes across multiple treatment rounds. A key innovation is the definition of sequence-specific entire contemporaneous eligible (ECE) populations, combined with weighted and post-stratification estimation, model-assisted covariate adjustment, and cluster-robust variance estimation. This approach effectively accounts for within-subject correlations induced by repeat enrollment while preserving the integrity of randomized comparisons and accommodating heterogeneous enrollment mechanisms. The method is validated through simulations and successfully applied to the SIMPLIFY trial in cystic fibrosis, with implementation available via the R package RobinCID.

Abstract

A master protocol trial uses a single overarching protocol to test multiple therapies, often across several diseases or subtypes. Although such trials offer considerable flexibility and efficiency, their constrained and non-uniform treatment assignment raises two core challenges: precisely defining treatment effects and conducting robust, efficient inference. These challenges intensify when participants can re-enroll to receive additional eligible therapies over time. To address these issues, we first define a clinically meaningful estimand with a clear population specification for master protocol trials that allow re-enrollment across multiple episodes. Specifically, we define the episode-specific entire concurrently eligible (ECE) population, which preserves the integrity of randomized comparisons and remains invariant to randomization ratios and operational formats. We then introduce a per-episode added-effect estimand that aggregates episode-specific effects into an interpretable overall measure. For inference, we develop weighting and post-stratification estimators under the same minimal assumptions as conventional randomized trials, with model-assisted covariate adjustment to improve efficiency. We establish asymptotic distributions for all estimators and provide cluster-robust variance estimators that properly account for within-participant correlation induced by re-enrollment. We evaluate our methods through extensive simulations and apply our methods to SIMPLIFY, a master protocol trial comparing continuation versus discontinuation of two common cystic fibrosis therapies. All analyses are conducted using the \textsf{R} package \textsf{RobinCID}.

Problem

• master protocol trials
• re-enrollment
• treatment effect estimation
• longitudinal patient histories
• statistical inference

Innovation

• master protocol trial
• re-enrollment
• estimand
• ECE population
• cluster-robust variance