发表机构
Yale School of Medicine; Yale School of Public Health; Monash University; The Ottawa Hospital Research Institute; University of Ottawa（耶鲁医学院; 耶鲁公共卫生学院; 蒙纳士大学; 渥太华医院研究所; 渥太华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过模拟评估多种分析方法在极少簇多时期整群随机交叉试验中的表现，发现准确建模簇-时期相关性比选择固定或随机截距更重要，且稀疏设计结果需谨慎解读。
AI 中文摘要
整群随机交叉（CRXO）试验通常在个体随机化不可行且可用簇数量有限时使用。然而，由于需要考虑随时间变化的复杂相关结构，CRXO试验的统计分析较为复杂。当使用的簇数量极少时，这一挑战尤为突出，因为标准建模假设可能导致方差估计不稳定、置信区间覆盖率差以及I型错误膨胀。本研究评估了含或不含簇-时期随机效应的个体水平混合效应模型和固定效应模型、基于正态或t分布的簇-时期汇总分析，以及两时期交叉差分估计量。通过在嵌套可交换和离散时间衰减相关结构下进行大量模拟研究，我们比较了各模型在偏倚、均方根误差、覆盖率概率、I型错误和收敛性方面的表现。在所有情景下，所有模型均产生了近似无偏的治疗效应估计，但其推断性能差异显著。明确考虑簇-时期异质性的模型通常能最可靠地控制覆盖率和I型错误，而更简单的可交换模型仅当真实相关结构与其假设高度匹配时表现充分。簇-时期水平分析的性能随时期数增加而改善，但在最稀疏的设计中不可靠。总体而言，研究结果表明，在簇数量极少的CRXO试验中，准确建模簇-时期相关性比选择固定或随机簇截距更为重要，且对极其稀疏设计的结果应谨慎解读。
英文摘要
Cluster randomized crossover (CRXO) trials are often used when individual randomization is impractical and the number of available clusters is limited. However, statistical analysis of CRXO trials is complex because of the need to account for complex correlation structures over time. It becomes especially challenging when very few clusters are used because standard modeling assumptions may lead to unstable variance estimates, poor confidence interval coverage, and inflated type I error. This study evaluates individual-level mixed-effects and fixed-effects models with and without a cluster-period random effect, cluster-period summary analysis using normal- or \(t\)-based inference, and two-period crossover-difference estimators. Using extensive simulation studies under both nested exchangeable and discrete time decay correlation structures, we compare model performance in terms of bias, root mean squared error, coverage probability, type I error, and convergence. Across scenarios, all models produced approximately unbiased treatment effect estimates, but their inferential performance differed substantially. Models that explicitly accounted for cluster-period heterogeneity generally provided the most reliable control of coverage and type I error, whereas simpler exchangeable models performed adequately only when the true correlation structure closely matched their assumptions. Cluster-period level analysis performance improved with increasing numbers of periods but was unreliable in the sparsest designs. Overall, the findings suggest that in CRXO trials with very few clusters, accurate modeling of cluster-period correlation is more important than the choice between fixed and random cluster intercepts, and that results from extremely sparse designs should be interpreted with caution.
