Evolving Longitudinal Patient Histories and Re-enrollment in Master Protocol Trials
主方案试验中的纵向患者历史与重复入组AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要围绕随机试验中的治疗效应目标、估计和重复入组条件下的因果比较展开。
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主方案试验中的纵向患者历史与重复入组
- 研究问题
- 允许患者多次入组并接受不同合格疗法的主方案试验，如何定义可解释的治疗效应，并正确处理重复参与造成的相关性？
- 方法与思路
- 作者定义每轮同期全部合格人群和每轮新增效应，构造加权、后分层及模型辅助估计，并使用参与者层面的聚类稳健方差。
- 材料陈述的贡献
- 摘要称，目标人群不随随机化比例或运行形式改变；作者建立估计量渐近分布，并通过模拟和 SIMPLIFY 试验展示方法。
- 阅读关注点
- 应核对每轮资格与人群的定义、效应汇总权重、历史治疗的处理，以及随机化和聚类方差所需的具体条件。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未获核实；摘要未列出所谓最小假设，也未报告模拟及实际试验的数值结果。
原文摘要
A master protocol trial uses a single overarching protocol to test multiple therapies, often across several diseases or subtypes. Although such trials offer considerable flexibility and efficiency, their constrained and non-uniform treatment assignment raises two core challenges: precisely defining treatment effects and conducting robust, efficient inference. These challenges intensify when participants can re-enroll to receive additional eligible therapies over time. To address these issues, we first define a clinically meaningful estimand with a clear population specification for master protocol trials that allow re-enrollment across multiple episodes. Specifically, we define the episode-specific entire concurrently eligible (ECE) population, which preserves the integrity of randomized comparisons and remains invariant to randomization ratios and operational formats. We then introduce a per-episode added-effect estimand that aggregates episode-specific effects into an interpretable overall measure. For inference, we develop weighting and post-stratification estimators under the same minimal assumptions as conventional randomized trials, with model-assisted covariate adjustment to improve efficiency. We establish asymptotic distributions for all estimators and provide cluster-robust variance estimators that properly account for within-participant correlation induced by re-enrollment. We evaluate our methods through extensive simulations and apply our methods to SIMPLIFY, a master protocol trial comparing continuation versus discontinuation of two common cystic fibrosis therapies. All analyses are conducted using the \textsf{R} package \textsf{RobinCID}.