Improving sandwich variance estimation for marginal Cox analysis of cluster randomized trials.
Improving sandwich variance estimation for marginal Cox analysis of cluster randomized trials.
复制标题
改进整群随机试验边际 Cox 分析的夹心方差估计。
DOI：
10.1002/bimj.202200113
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Li,Fan
中科院分区：
文献类型：
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作者：
Wang,Xueqi;Turner,ElizabethL;Li,Fan
集群随机试验(CRT)经常招募少量的集群，因此有必要应用小样本校正来进行有效的推断。最近的一项系统评价表明，CRT报告权利审查、事件发生时间结果的情况并不少见，边际COX比例风险模型是用于初步分析的常见方法之一。虽然小样本校正已经在具有连续、二元和计数结果的边际模型下进行了研究，但在用边际COX模型分析集群化的事件间隔结果时，还没有先前的研究致力于开发和评估偏差校正夹心方差估计量。为了改进目前的实践，我们提出了9个偏差校正夹心方差估计，用于使用边际Cox模型分析CRT，并报告了一项模拟研究以评估它们的小样本性质。我们的结果表明，对于有生存结果的CRT，偏差校正夹心方差估计器的最佳选择可以取决于簇大小的变异性，也可以略有不同，无论是根据相对偏差还是根据I型错误率进行评估。最后，我们在真实世界的CRT中说明了新的方差估计，其中关于干预有效性的结论取决于小样本偏差校正的使用。所提出的夹心方差估计在R包CoxBcv中实现。
Cluster randomized trials (CRTs) frequently recruit a small number of clusters, therefore necessitating the application of small‐sample corrections for valid inference. A recent systematic review indicated that CRTs reporting right‐censored, time‐to‐event outcomes are not uncommon and that the marginal Cox proportional hazards model is one of the common approaches used for primary analysis. While small‐sample corrections have been studied under marginal models with continuous, binary, and count outcomes, no prior research has been devoted to the development and evaluation of bias‐corrected sandwich variance estimators when clustered time‐to‐event outcomes are analyzed by the marginal Cox model. To improve current practice, we propose nine bias‐corrected sandwich variance estimators for the analysis of CRTs using the marginal Cox model and report on a simulation study to evaluate their small‐sample properties. Our results indicate that the optimal choice of bias‐corrected sandwich variance estimator for CRTs with survival outcomes can depend on the variability of cluster sizes and can also slightly differ whether it is evaluated according to relative bias or type I error rate. Finally, we illustrate the new variance estimators in a real‐world CRT where the conclusion about intervention effectiveness differs depending on the use of small‐sample bias corrections. The proposed sandwich variance estimators are implemented in an R packageCoxBcv.