PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■在具有二元终点的集群随机试验中，治疗组和集群之间的异质性结果相关性越来越得到认可。已经开发了分析方法来研究这种异质性。然而,对于具有连续结局的整群随机试验,尚未研究整群特异性结局差异和相关性.
■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，以量化跨集群的异质方差，并在结果连续时使用集群级别的协变量进行解释。该模型还可以扩展到分析单独随机组治疗试验中的异质性差异，使用手臂特定的集群级别的协变量，或部分嵌套的设计。进行了仿真研究，以验证新引入的模型在不同设置中的性能。
■仿真表明，总体而言，新推出的模型具有良好的性能，报告方差模型中的组内相关系数和回归参数的偏差较低,覆盖率约为95%.当差异是异质的时，与具有齐次方差的模型相比，我们提出的模型改进了模型拟合。当用于分析喀拉拉邦糖尿病预防计划研究的数据时，我们的模型识别了不同聚类的异质性方差和组内相关系数,并检查了与这种异质性相关的聚类水平特征.
■我们提出了新的分层贝叶斯方差模型，以适应集群随机试验中特定于集群的方差。新开发的方法有助于理解如何在集群中实施和传播干预策略，并有助于改进未来的试验设计。
■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，以量化跨集群的异质方差，并在结果连续时使用集群级别的协变量进行解释。该模型还可以扩展到分析单独随机组治疗试验中的异质性差异，使用手臂特定的集群级别的协变量，或部分嵌套的设计。进行了仿真研究，以验证新引入的模型在不同设置中的性能。
■仿真表明，总体而言，新推出的模型具有良好的性能，报告方差模型中的组内相关系数和回归参数的偏差较低,覆盖率约为95%.当差异是异质的时，与具有齐次方差的模型相比，我们提出的模型改进了模型拟合。当用于分析喀拉拉邦糖尿病预防计划研究的数据时，我们的模型识别了不同聚类的异质性方差和组内相关系数,并检查了与这种异质性相关的聚类水平特征.
■我们提出了新的分层贝叶斯方差模型，以适应集群随机试验中特定于集群的方差。新开发的方法有助于理解如何在集群中实施和传播干预策略，并有助于改进未来的试验设计。
