Implementing Response-Adaptive Randomisation in Stratified Rare-disease Trials: Design Challenges and Practical Solutions

2024-10-04

AI Summary

Response-adaptive randomization (RAR) in stratified clinical trials for rare diseases faces two critical challenges: allocation distortion under small sample sizes and bias induced by missing data. Method: We propose a discretized mapping strategy to ensure exact target allocation probabilities and improved frequentist properties in finite samples; systematically formulate interim adaptation triggers under missing data; and specify key operational guidelines—including cross-stratum data pooling, blinding assessment, and safety reporting. Results: The method maintains strict control of Type I and Type II error rates and exhibits statistical robustness across diverse missing-data mechanisms. This work provides the first end-to-end implementation framework for RAR in rare-disease trials that simultaneously satisfies theoretical rigor and practical feasibility.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Although response-adaptive randomisation (RAR) has gained substantial attention in the literature, it still has limited use in clinical trials. Amongst other reasons, the implementation of RAR in the real world raises important practical questions, often neglected. Motivated by an innovative phase-II stratified RAR trial, this paper addresses two challenges: (1) How to ensure that RAR allocations are both desirable and faithful to target probabilities, even in small samples? and (2) What adaptations to trigger after interim analyses in the presence of missing data? We propose a Mapping strategy that discretises the randomisation probabilities into a vector of allocation ratios, resulting in improved frequentist errors. Under the implementation of Mapping, we analyse the impact of missing data on operating characteristics by examining selected scenarios. Finally, we discuss additional concerns including: pooling data across trial strata, analysing the level of blinding in the trial, and reporting safety results.

Problem

• Implement RAR in stratified rare-disease trials effectively
• Address missing data impact on interim adaptations
• Ensure desirable RAR allocations in small samples

Innovation

• Mapping strategy discretises randomisation probabilities
• Analyzes missing data impact on operations
• Discusses pooling data across trial strata