Platform trials are multi-arm designs that simultaneously evaluate multiple treatments for a single disease within the same overall trial structure. Unlike traditional randomized controlled trials, they allow treatment arms to enter and exit the trial at distinct times while maintaining a control arm throughout. This control arm comprises both concurrent controls, where participants are randomized concurrently to either the treatment or control arm, and non-concurrent controls, who enter the trial when the treatment arm under study is unavailable. While flexible, platform trials introduce the challenge of using non-concurrent controls, raising questions about estimating treatment effects. Specifically, which estimands should be targeted? Under what assumptions can these estimands be identified and estimated? Are there any efficiency gains? In this paper, we discuss issues related to the identification and estimation assumptions of common choices of estimand. We conclude that the most robust strategy to increase efficiency without imposing unwarranted assumptions is to target the concurrent average treatment effect (cATE), the ATE among only concurrent units, using a covariate-adjusted doubly robust estimator. Our studies suggest that, for the purpose of obtaining efficiency gains, collecting important prognostic variables is more important than relying on non-concurrent controls. We also discuss the perils of targeting ATE due to an untestable extrapolation assumption that will often be invalid. We provide simulations illustrating our points and an application to the ACTT platform trial, resulting in a 20% improvement in precision compared to the naive estimator that ignores non-concurrent controls and prognostic variables.

翻译：平台试验是一种多臂设计，可在同一整体试验框架内同时评估针对单一疾病的多种治疗方案。与传统随机对照试验不同，平台试验允许各治疗臂在不同时间点进入或退出试验，同时在整个过程中维持一个对照臂。该对照臂包含同期对照（参与者被随机分配至治疗臂或对照臂的时间点相同）和非同期对照（在研究中的治疗臂不可用时进入试验）。尽管具有灵活性，平台试验引入了使用非同期对照的挑战，引发了关于治疗效果估计的问题。具体而言：应针对何种估计量？在何种假设下这些估计量可被识别和估计？是否存在效率提升？本文讨论了与常见估计量选择相关的识别和估计假设问题。我们得出结论：在不施加无依据假设的前提下提高稳健性的最优策略是，使用协变量调整的双稳健估计量来估计同期平均处理效应（cATE），即仅针对同期单元的ATE。研究表明，为获得效率提升，收集重要的预后变量比依赖非同期对照更为关键。本文还讨论了因不可检验的外推假设（通常无效）而估计ATE的风险。我们通过模拟实验阐明观点，并应用于ACTT平台试验案例，结果显示：相较于忽略非同期对照和预后变量的朴素估计量，该方法使估计精度提升了20%。