Dynamic treatment regimes (DTRs) are sequences of decision rules to guide treatment assignments in response to a patient's evolving, time-varying disease status. Sequential multiple assignment randomized trials (SMARTs) are considered the gold standard experimental design for evaluating DTRs. However, SMARTs often require more time to complete compared with a single stage RCT and new candidate treatments may become available or feasible during the trial. Platform trials are an adaptive trial design that allow new treatments to be added to the ongoing study according to a prespecified master protocol. In this paper, we introduce a novel platform SMART that integrates features from both platform trials and SMARTs, allowing new treatments to be added during the trial. Additionally, we propose the Bayesian integration G-formula (BIG) estimators for platform SMARTs to account for non-concurrent treatment comparisons. Extensive simulations are conducted to evaluate the performance of different BIG estimators against benchmark methods. We demonstrate the proposed BIG estimators based on the S. aureus Network Adaptive Platform (SNAP) trial.

翻译：动态治疗方案（DTRs）是一系列根据患者随时间变化的疾病状态指导治疗分配的决策规则序列。序贯多分配随机试验（SMARTs）被认为是评估DTRs的金标准实验设计。然而，与单阶段随机对照试验相比，SMARTs通常需要更长的完成时间，且在新候选治疗可能在试验期间变得可行或可及。平台试验是一种适应性试验设计，允许根据预先设定的主方案向正在进行的试验添加新治疗。本文介绍了一种新型平台SMART，它整合了平台试验和SMART的特征，允许在试验进行期间添加新治疗。此外，我们提出了适用于平台SMART的贝叶斯集成G公式（BIG）估计量，以处理非并发治疗比较。通过大量模拟实验，我们评估了不同BIG估计量相对于基准方法的性能。我们基于金黄色葡萄球菌网络适应性平台（SNAP）试验对所提出的BIG估计量进行了验证。