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

TL;DR

This paper explores practical challenges and solutions for implementing response-adaptive randomisation in stratified rare-disease trials, focusing on allocation desirability and handling missing data.

Contribution

It introduces a Mapping strategy to improve allocation fidelity and analyzes the impact of missing data on trial operating characteristics.

Findings

Mapping improves frequentist error control

Handling missing data affects trial outcomes

Practical solutions enhance RAR implementation

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 real world trials raises important practical questions, often neglected in the technical literature. Motivated by an innovative phase-II stratified RAR rare-disease trial, this paper addresses two challenges: (1) How to ensure that RAR allocations are desirable i.e. both acceptable and faithful to the intended probabilities, particularly in small samples? and (2) What adaptations to trigger after interim analyses in the presence of missing data? To answer (1), 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 answer (2) by analysing the impact of missing data on operating characteristics in 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.