Background:Heterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster randomized trials with continuous outcomes.Methods:This article proposes models fitted in the Bayesian setting with hierarchical variance structure to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings.Results:Simulations showed that overall the newly introduced models have good performance, reporting low bias and approximately 95% coverage for the intraclass correlation coefficients and regression parameters in the variance model. When variances are heterogeneous, our proposed models had improved model fit over models with homogeneous variances. When used to analyze data from the Kerala Diabetes Prevention Program study, our models identified heterogeneous variances and intraclass correlation coefficients across clusters and examined cluster-level characteristics associated with such heterogeneity.Conclusion:We proposed new hierarchical Bayesian variance models to accommodate cluster-specific variances in cluster randomized trials. The newly developed methods inform the understanding of how an intervention strategy is implemented and disseminated differently across clusters and can help improve future trial design.

中文翻译：

---

整群随机试验中异质结果方差的分层贝叶斯模型

背景：在具有二元终点的整群随机试验中，治疗组和整群之间的异质结果相关性已得到越来越多的认可，其中已经开发了分析方法来研究这种异质性。然而，对于具有连续结果的聚类随机试验，尚未研究特定于聚类的结果方差和相关性。方法：本文提出了在贝叶斯设置中拟合的具有分层方差结构的模型，以量化聚类之间的异质方差并用聚类级别进行解释当结果是连续的时，协变量。该模型还可以扩展到分析单独随机组治疗试验中的异质方差，使用特定臂的聚类水平协变量或部分嵌套设计。进行了模拟研究，以验证新引入的模型在不同设置下的性能。结果：模拟表明，总体而言，新引入的模型具有良好的性能，报告偏差较低，组内相关系数和回归参数的覆盖率约为 95%。方差模型。当方差异质时，我们提出的模型比具有同质方差的模型具有改进的模型拟合度。当用于分析喀拉拉邦糖尿病预防计划研究的数据时，我们的模型识别了跨簇的异质方差和类内相关系数，并检查了与这种异质性相关的簇级特征。结论：我们提出了新的分层贝叶斯方差模型来适应簇特定的方差在整群随机试验中。新开发的方法有助于了解干预策略如何在不同集群之间以不同方式实施和传播，并有助于改进未来的试验设计。