Bayesian latent variable models for clustered mixed outcomes
Bayesian latent variable models for clustered mixed outcomes
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DOI：
10.1111/1467-9868.00236
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发表时间：
2000-01-01
影响因子：
5.8
通讯作者：
Dunson, DB
中科院分区：
文献类型：
--
作者：
Dunson, DB
提出了聚类混合结果建模的一般框架。混合广义线性模型用于描述一组基础变量的联合分布，并将基础变量与观测结果联系起来的任意函数。该模型可容纳多层数据结构、一般协变量效应以及每个底层变量的不同链接函数和误差分布。在此框架下，针对聚类多元二值、无序分类和联合离散连续结果开发了新的模型。描述了一种马尔可夫链蒙特卡罗采样算法，用于估计参数和潜在变量的后验分布。由于建模框架和估计过程的灵活性，对有序分类结果和更复杂的数据结构的扩展是直接的。这些方法用一项生殖毒性研究的数据加以说明。
A general framework is proposed for modelling clustered mixed outcomes. A mixture of generalized linear models is used to describe the joint distribution of a set of underlying variables, and an arbitrary function relates the underlying variables to the observed outcomes. The model accommodates multilevel data structures, general covariate effects and distinct link functions and error distributions for each underlying variable. Within the framework proposed, novel models are developed for clustered multiple binary, unordered categorical and joint discrete and continuous outcomes. A Markov chain Monte Carlo sampling algorithm is described for estimating the posterior distributions of the parameters and latent variables. Because of the flexibility of the modelling framework and estimation procedure, extensions to ordered categorical outcomes and more complex data structures are straightforward. The methods are illustrated by using data from a reproductive toxicity study.