标题： 滚动 enroll（注册） 入学情况下匹配的稳健推断 显示英文标题

标题： Robust inference for matching under rolling enrollment

摘要： 在观察性研究中，当受试者基于滚动基础加入治疗时，匹配面临复杂情况。每个接受治疗的受试者都有特定的研究进入时间，而对照组的每个受试者都有许多可能的比较时间，或称“伪治疗”时间。最近提出的GroupMatch框架（Pimentel等人，2020年）通过为每个对照组受试者搜索所有可能的伪治疗时间，并选择基于协变量历史允许最接近匹配的时间，解决了这一问题。然而，仅对于General GroupMatch设计的特殊情况描述了有效的推断方法，且这些方法依赖于强假设。我们提供了三个重要的创新来解决这些问题。首先，我们引入了一种新的设计——带有实例替换的GroupMatch，它在对照组选择上提供了额外的灵活性，并更易于分析。其次，我们提出了一个块状自助法（block bootstrap）方法用于带实例替换的GroupMatch中的推断，并证明它可以正确处理匹配集之间的复杂相关性。第三，我们开发了一种基于排列的验证测试，以检测支撑GroupMatch的重要时间点无知假设可能存在的违反情况，该假设要求潜在结果均值在时间上具有同质性。通过模拟和一项关于短期伤病对职业棒球大联盟击球表现影响的案例研究，我们展示了我们的方法在实际数据分析中的有效性。 显示英文摘要

摘要： Matching in observational studies faces complications when units enroll in treatment on a rolling basis. While each treated unit has a specific time of entry into the study, control units each have many possible comparison, or "pseudo-treatment," times. The recent GroupMatch framework (Pimentel et al., 2020) solves this problem by searching over all possible pseudo-treatment times for each control and selecting those permitting the closest matches based on covariate histories. However, valid methods of inference have been described only for special cases of the general GroupMatch design, and these rely on strong assumptions. We provide three important innovations to address these problems. First, we introduce a new design, GroupMatch with instance replacement, that allows additional flexibility in control selection and proves more amenable to analysis. Second, we propose a block bootstrap approach for inference in GroupMatch with instance replacement and demonstrate that it accounts properly for complex correlations across matched sets. Third, we develop a permutation-based falsification test to detect possible violations of the important timepoint agnosticism assumption underpinning GroupMatch, which requires homogeneity of potential outcome means across time. Via simulation and a case study of the impact of short-term injuries on batting performance in major league baseball, we demonstrate the effectiveness of our methods for data analysis in practice.