A Bayesian approach for joint modeling of cluster size and subunit-specific outcomes
A Bayesian approach for joint modeling of cluster size and subunit-specific outcomes
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DOI：
10.1111/1541-0420.00062
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发表时间：
2003-09-01
期刊：
影响因子：
1.9
通讯作者：
Harry, J
中科院分区：
文献类型：
--
作者：
Dunson, DB;Chen, Z;Harry, J
在涉及聚类数据的应用中，如纵向研究和发育毒性实验，聚类中亚基的数量通常与对单个亚基的测量结果相关。忽略这种依赖性的分析可能会产生有偏见的推论。本文提出了一个贝叶斯框架，用于联合建模聚类大小和在每个子单元上测量的多个分类和连续结果。我们对聚类大小使用延续比概率模型，对每个亚单元特定的结果使用潜在的正态回归模型。集群大小和不同结果之间的依赖关系通过潜在变量结构进行调节。该模型的形式便于通过简单且计算效率高的吉布斯采样器进行后验计算。该方法以发育毒性数据的应用为例加以说明，并讨论了纵向和事件时间数据联合建模的其他应用。
In applications that involve clustered data, such as longitudinal studies and developmental toxicity experiments, the number of subunits within a cluster is often correlated with outcomes measured on the individual subunits. Analyses that ignore this dependency can produce biased inferences. This article proposes a Bayesian framework for jointly modeling cluster size and multiple categorical and continuous outcomes measured on each subunit. We use a continuation ratio probit model for the cluster size and underlying normal regression models for each of the subunit-specific outcomes. Dependency between cluster size and the different outcomes is accommodated through a latent variable structure. The form of the model facilitates posterior computation via a simple and computationally efficient Gibbs sampler. The approach is illustrated with an application to developmental toxicity data, and other applications, to joint modeling of longitudinal and event time data, are discussed.