Adaptive Randomization Methods for Sequential Multiple Assignment Randomized Trials (SMARTs) via Thompson Sampling
arxiv(2024)
摘要
Response-adaptive randomization (RAR) has been studied extensively in
conventional, single-stage clinical trials, where it has been shown to yield
ethical and statistical benefits, especially in trials with many treatment
arms. However, RAR and its potential benefits are understudied in sequential
multiple assignment randomized trials (SMARTs), which are the gold-standard
trial design for evaluation of multi-stage treatment regimes. We propose a
suite of RAR algorithms for SMARTs based on Thompson Sampling (TS), a widely
used RAR method in single-stage trials in which treatment randomization
probabilities are aligned with the estimated probability that the treatment is
optimal. We focus on two common objectives in SMARTs: (i) comparison of the
regimes embedded in the trial, and (ii) estimation of an optimal embedded
regime. We develop valid post-study inferential procedures for treatment
regimes under the proposed algorithms. This is nontrivial, as (even in
single-stage settings) RAR can lead to nonnormal limiting distributions of
estimators. Our algorithms are the first for RAR in multi-stage trials that
account for nonregularity in the estimand. Empirical studies based on
real-world SMARTs show that TS can improve in-trial subject outcomes without
sacrificing efficiency for post-trial comparisons.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要