Dunson David B, Chen Zhen, Harry Jean
Biostatistics Branch, MD A3-03, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, North Carolina 27709, USA.
Biometrics. 2003 Sep;59(3):521-30. doi: 10.1111/1541-0420.00062.
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.
在涉及聚类数据的应用中，如纵向研究和发育毒性实验，聚类内的亚单位数量通常与在各个亚单位上测量的结果相关。忽略这种依赖性的分析可能会产生有偏差的推断。本文提出了一个贝叶斯框架，用于联合建模聚类大小以及在每个亚单位上测量的多个分类和连续结果。我们对聚类大小使用连续比例概率模型，对每个亚单位特定的结果使用潜在正态回归模型。聚类大小与不同结果之间的依赖性通过一个潜在变量结构来处理。该模型的形式通过一个简单且计算高效的吉布斯采样器便于后验计算。通过对发育毒性数据的应用来说明该方法，并讨论了其他应用，即对纵向和事件时间数据的联合建模。
