A Markov Decision Process for Response-Adaptive Randomization in Clinical Trials

TL;DR

This paper introduces TrialMDP, a dynamic programming-based algorithm that optimizes response-adaptive randomization in clinical trials by adaptively determining block sizes and allocations to improve utility, balancing power and patient outcomes.

Contribution

It presents a novel algorithm that optimizes block sizes and treatment allocations in response-adaptive randomization, outperforming traditional fixed-block methods.

Findings

Significant utility improvements over baseline designs.

Effective control over power and patient outcomes tradeoff.

Suitable for small, high-cost trials.

Abstract

In clinical trials, response-adaptive randomization (RAR) has the appealing ability to assign more subjects to better-performing treatments based on interim results. The traditional RAR strategy alters the randomization ratio on a patient-by-patient basis; this has been heavily criticized for bias due to time-trends. An alternate approach is blocked RAR, which groups patients together in blocks and recomputes the randomization ratio in a block-wise fashion; the final analysis is then stratified by block. However, the typical blocked RAR design divides patients into equal-sized blocks, which is not generally optimal. This paper presents TrialMDP, an algorithm that designs two-armed blocked RAR clinical trials. Our method differs from past approaches in that it optimizes the size and number of blocks as well as their treatment allocations. That is, the algorithm yields a policy that adaptively chooses the size and composition of the next block, based on results seen up to that point in the trial. TrialMDP is related to past works that compute optimal trial designs via dynamic programming. The algorithm maximizes a utility function balancing (i) statistical power, (ii) patient outcomes, and (iii) the number of blocks. We show that it attains significant improvements in utility over a suite of baseline designs, and gives useful control over the tradeoff between statistical power and patient outcomes. It is well suited for small trials that assign high cost to failures. We provide TrialMDP as an R package on GitHub: https://github.com/dpmerrell/TrialMDP