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.

翻译：配对整群随机实验（pCRTs）在许多学科中都很常见，因为个体通常存在自然聚类，而配对随机化有助于平衡基线协变量以提高实验精度。尽管pCRTs应用广泛，但令人惊讶的是，当关注个体层面（而非整群层面）的处理效应时，目前尚无分析该随机化设计的明确方法。由于整群配对产生的依赖性，方差估计也较为复杂。因此，我们旨在对pCRTs中不同估计策略进行直观实用的比较，以指导实践者选择策略。为此，我们提出了一个针对平均个体效应的pCRTs基于设计估计的通用框架。该框架为估计量间的偏差-方差权衡提供了新颖而直观的视角，并强调了协变量调整在pCRTs估计中的优势。除了提供pCRTs估计的通用框架外，我们提出的点估计量和方差估计量支持固定样本无偏估计，其精度与常见回归模型相当，并能实现一致保守的方差估计。通过模拟研究，我们比较了所综述点估计量与方差估计量的性能。最后，我们利用教育效能试验的真实数据通过模拟比较了各估计量的表现。我们的分析与模拟研究为实践中分析pCRTs时选择点估计量和方差估计量提供了依据。