标题： 一种针对配对群组随机试验中基于设计的处理效应估计的一般框架 显示英文标题

标题： A General Framework for Design-Based Treatment Effect Estimation in Paired Cluster-Randomized Experiments

摘要： 配对群组随机试验（pCRTs）在许多学科中都很常见，因为个体通常存在自然的群组划分，并且配对随机化可以帮助平衡基线协变量以提高实验精度。 尽管pCRTs很常见，但如果关注的是个体层面（而非群组层面）的治疗效果，目前并没有明显直观的方法来分析这种随机化设计。 由于通过配对群组创建了依赖性，方差估计也变得复杂。 因此，我们旨在提供一种直观且实用的比较，以比较pCRTs中的不同估计策略，从而为实践者选择策略提供信息。 为此，我们提出了一个基于设计的估计框架，用于平均个体效应的pCRTs。 该框架提供了对估计量之间偏差-方差权衡的新颖且直观的视角，并强调了协变量调整对pCRTs估计的好处。 除了为pCRTs的估计提供一个一般框架外，我们提出的点估计和方差估计支持固定样本无偏估计，并具有与常见回归模型相似的精度，以及一致保守的方差估计。 通过模拟研究，我们比较了所审查的点估计和方差估计的表现。 最后，我们使用教育成效试验的真实数据进行了模拟，比较了估计量的表现。 我们的分析和模拟研究为实际分析pCRTs时点估计和方差估计的选择提供了参考。 显示英文摘要

摘要： Paired cluster-randomized experiments (pCRTs) are common across many disciplines because there is often natural clustering of individuals, and paired randomization can help balance baseline covariates to improve experimental precision. Although pCRTs are common, there is surprisingly no obvious way to analyze this randomization design if an individual-level (rather than cluster-level) treatment effect is of interest. Variance estimation is also complicated due to the dependency created through pairing clusters. Therefore, we aim to provide an intuitive and practical comparison between different estimation strategies in pCRTs in order to inform practitioners' choice of strategy. To this end, we present a general framework for design-based estimation in pCRTs for average individual effects. This framework offers a novel and intuitive view on the bias-variance trade-off between estimators and emphasizes the benefits of covariate adjustment for estimation with pCRTs. In addition to providing a general framework for estimation in pCRTs, the point and variance estimators we present support fixed-sample unbiased estimation with similar precision to a common regression model and consistently conservative variance estimation. Through simulation studies, we compare the performance of the point and variance estimators reviewed. Finally, we compare the performance of estimators with simulations using real data from an educational efficacy trial. Our analysis and simulation studies inform the choice of point and variance estimators for analyzing pCRTs in practice.