Norwood Peter, Davidian Marie, Laber Eric
Quantum Leap Healthcare Collaborative, 499 Illinois Ave, Suite 200, San Francisco, CA 94158, United States.
Department of Statistics, North Carolina State University, 2311 Stinson Drive, Campus Box 8203, Raleigh, NC 27695-8203, United States.
Biometrics. 2024 Oct 3;80(4). doi: 10.1093/biomtc/ujae152.
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: (1) comparison of the regimes embedded in the trial and (2) 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 standard estimators of an average treatment effect can have nonnormal asymptotic behavior under RAR. Our algorithms are the first for RAR in multi-stage trials that account for non-standard limiting behavior due to RAR. Empirical studies based on real-world SMARTs show that TS can improve in-trial subject outcomes without sacrificing efficiency for post-trial comparisons.
响应自适应随机化（RAR）已在传统的单阶段临床试验中得到广泛研究，研究表明它能带来伦理和统计方面的益处，尤其是在有多个治疗组的试验中。然而，在序贯多重分配随机试验（SMARTs）中，RAR及其潜在益处尚未得到充分研究，而SMARTs是评估多阶段治疗方案的金标准试验设计。我们基于汤普森抽样（TS）提出了一套适用于SMARTs的RAR算法，TS是单阶段试验中广泛使用的RAR方法，其中治疗随机化概率与治疗最优的估计概率一致。我们关注SMARTs中的两个常见目标：（1）试验中所包含方案的比较；（2）最优包含方案的估计。我们为所提出的算法开发了针对治疗方案的有效研究后推断程序。这并非易事，因为即使在单阶段设置中，平均治疗效果的标准估计量在RAR下也可能具有非正态渐近行为。我们的算法是多阶段试验中首个考虑到RAR导致的非标准极限行为的RAR算法。基于真实世界SMARTs的实证研究表明，TS可以改善试验中受试者的结果，而不会牺牲试验后比较的效率。
