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 a unique challenge with the use of non-concurrent controls, raising questions about how to efficiently utilize their data to estimate treatment effects. Specifically, what estimands should be used to evaluate the causal effect of a treatment versus control? Under what assumptions can these estimands be identified and estimated? Do we achieve any efficiency gains? In this paper, we use structural causal models and counterfactuals to clarify estimands and formalize their identification in the presence of non-concurrent controls in platform trials. We also provide outcome regression, inverse probability weighting, and doubly robust estimators for their estimation. We discuss efficiency gains, demonstrate their performance in a simulation study, and apply them to the ACTT platform trial, resulting in a 20% improvement in precision.

翻译：平台试验是一种多臂设计，能在同一总体试验结构下同时评估针对单一疾病的多种治疗方法。与传统随机对照试验不同，平台试验允许治疗组在不同时间点加入或退出试验，同时保留贯穿全程的对照组。该对照组包含两类参与者：同期对照组（参与者被随机分配到治疗组或对照组）和非同期对照组（参与者在研究中的治疗组尚未开放时进入试验）。尽管具有灵活性，但平台试验在利用非同期对照组时引入独特挑战，引发以下问题：应使用何种估计量评估治疗与对照的因果效应？在何种假设下这些估计量可被识别和估计？能否实现效率提升？本文采用结构因果模型和反事实框架，阐明非同期对照组存在时平台试验中的估计量定义并形式化其识别过程。我们进一步提出结果回归、逆概率加权及双重稳健估计方法，讨论效率提升机制，并通过仿真研究验证性能。将方法应用于ACTT平台试验后，精确度提升20%。