Inference in Cluster Randomized Trials with Matched Pairs
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AI 根据所提供的论文材料归类：摘要研究配对簇随机试验中的处理效应估计、随机化检验及协变量调整。
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配对簇随机试验中的推断
- 研究问题
- 簇按基线特征配对后在对内随机分配处理时，是否匹配簇规模会如何影响加权均值差、方差估计及常用检验的有效性？
- 方法与思路
- 作者分别分析匹配与不匹配簇规模的渐近情形，提出统一方差估计，并研究回归检验、对内随机化检验和协变量调整。
- 材料陈述的贡献
- 摘要称统一方差估计在两类情形下一致，相关检验渐近精确；另给出随机化检验有效性及协变量调整严格提高精度的条件。
- 阅读关注点
- 核对加权均值差的目标效应、簇规模匹配如何改变理论、随机化检验对应的零假设，以及协变量调整提高精度所需条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实。摘要未列渐近条件和具体零假设，不能认定随机化检验对所有效应假设均有效。
原文摘要
This paper studies inference in cluster randomized trials where treatment status is determined according to a "matched pairs" design. Here, by a cluster randomized experiment, we mean one in which treatment is assigned at the level of the cluster; by a "matched pairs" design, we mean that a sample of clusters is paired according to baseline, cluster-level covariates and, within each pair, one cluster is selected at random for treatment. We study the large-sample behavior of a weighted difference-in-means estimator and derive two distinct sets of results depending on if the matching procedure does or does not match on cluster size. We then propose a single variance estimator which is consistent in either regime. Combining these results establishes the asymptotic exactness of tests based on these estimators. Next, we consider the properties of two common testing procedures based on t-tests constructed from linear regressions, and argue that both are generally conservative in our framework. We additionally study the behavior of a randomization test which permutes the treatment status for clusters within pairs, and establish its finite-sample and asymptotic validity for testing specific null hypotheses. Finally, we propose a covariate-adjusted estimator which adjusts for additional baseline covariates not used for treatment assignment, and establish conditions under which such an estimator leads to strict improvements in precision. A simulation study confirms the practical relevance of our theoretical results.