From Estimands to Robust Inference of Treatment Effects in Master Protocol Trials
主方案试验：从目标量定义到治疗效应的稳健推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要围绕随机试验的因果目标量、治疗分配及稳健治疗效应估计展开。
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主方案试验：从目标量定义到治疗效应的稳健推断
- 研究问题
- 在个体可进入的干预组不同、分配比例不均的主方案试验中，如何定义稳定的目标人群并进行稳健有效推断？
- 方法与思路
- 作者定义完整同期合格试验人群，采用加权及后分层估计治疗效应，并通过模型辅助协变量调整改善估计效率。
- 材料陈述的贡献
- 摘要称，所定义目标人群不随随机化比例改变，方法保留随机比较的解释并对辅助模型误设稳健，另有试验应用。
- 阅读关注点
- 应核对同期合格人群的精确定义、各干预比较的可识别范围，以及加权、后分层与协变量调整所需的具体条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出所谓最小假设，也未说明资格变化下的估计细节。
原文摘要
Master protocol trials use a single overarching protocol to evaluate multiple interventions, diseases, or disease subtypes, where individuals are often randomized to different subsets of intervention arms based on individual characteristics, enrollment timing, and intervention availability. While offering increased flexibility, this constrained and non-uniform intervention assignment poses two fundamental inferential challenges: the precise definition of treatment effects and robust, efficient inference on these effects. These challenges arise primarily because some commonly used analysis approaches may target estimands defined on populations that inadvertently depend on the intervention allocation ratio, making them impossible to fully pre-specify, thereby undermining interpretability and opening the door to ambiguity, post-hoc decisions, and potential bias. This article, for the first time, presents a formal estimand framework for master protocol trials with precise specification of the population. The proposed entire concurrently eligible (ECE) trial population not only preserves the integrity of randomized comparisons but also remains invariant to the randomization ratio. Then, we develop weighting and post-stratification methods to estimate treatment effects under the same minimal assumptions used in traditional randomized trials. We also consider model-assisted covariate adjustment to fully unlock the efficiency potential of master protocol trials while maintaining robustness against model misspecification. The SIMPLIFY trial, a master protocol assessing continuation versus discontinuation of two common therapies in cystic fibrosis, is utilized to highlight the practical significance of this research. All analyses are conducted using the R package RobinCID.