Continuous monitoring of delayed outcomes in basket trials

2026-01-01

AI Summary

This study addresses the challenge of delayed treatment effects in basket trials—particularly with immunotherapies—where conventional interim analyses struggle to timely discontinue ineffective arms, and existing Bayesian approaches are computationally intensive and ill-suited for delayed outcomes. The authors propose a computationally efficient, continuous monitoring framework that, for the first time, integrates Bayesian empirical methods with multiple imputation to adaptively select the optimal strategy for handling missing data, thereby enabling effective interim decision-making under outcome delay. Simulation results demonstrate that when accrual is slow and missingness is minimal, simple approaches suffice; however, in complex settings involving multiple baskets and agents, the proposed method substantially improves sample utilization and overall trial efficiency.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Game Theory and Economic Paradigms: Imperfect Information
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Precision medicine has led to a paradigm shift allowing the development of targeted drugs that are agnostic to the tumor location. In this context, basket trials aim to identify which tumor types - or baskets - would benefit from the targeted therapy among patients with the same molecular marker or mutation. We propose the implementation of continuous monitoring for basket trials to increase the likelihood of early identification of non-promising baskets. Although the current Bayesian trial designs available in the literature can incorporate more than one interim analysis, most of them have high computational cost, and none of them handle delayed outcomes that are expected for targeted treatments such as immunotherapies. We leverage the Bayesian empirical approach proposed by Fujiwara et al., which has low computational cost. We also extend ideas of Cai et al to address the practical challenge of performing interim analysis with delayed outcomes using multiple imputation. Operating characteristics of four different strategies to handle delayed outcomes in basket trials are compared in an extensive simulation study with the benchmark strategy where trial accrual is put on hold until complete data is observed to make a decision. The optimal handling of missing data at interim analyses is trial-dependent. With slow accrual, missingness is minimal even with continuous monitoring, favoring simpler approaches over computationally intensive methods. Although individual sample-size savings are small, multiple imputation becomes more appealing when sample size savings scale with the number of baskets and agents tested.

Problem

• basket trials
• delayed outcomes
• interim analysis
• targeted therapy
• missing data

Innovation

• basket trials
• delayed outcomes
• continuous monitoring
• multiple imputation
• Bayesian empirical approach