Identification and estimation of causal effects using non-concurrent controls in platform trials
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平台试验中利用非同期对照识别与估计因果效应
- 研究问题
- 平台试验允许处理臂在不同时点进入或退出，如何选择目标效应并使用非同期对照，同时避免不可靠的跨时期外推并争取效率收益？
- 方法与思路
- 作者比较常见目标效应的识别与估计要求，主张以同期参与者的平均处理效应为目标，使用协变量调整的双重稳健估计量。
- 材料陈述的贡献
- 摘要称收集重要预后变量比依赖非同期对照更有助于提升效率；ACTT应用相较忽略非同期对照和预后变量的朴素估计精度提高20%。
- 阅读关注点
- 应核对同期平均处理效应的目标人群、非同期数据如何进入估计、双重稳健与效率提升的条件，以及总体平均效应所需的外推假设。
- 阅读边界
- 这是arXiv预印本，不能据此认定已同行评审或期刊接收。摘要未给出具体识别条件，20%的精度改善仅是所述应用中的比较结果。
原文摘要
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.