A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use.
A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use.
复制标题
DOI：
10.1177/1471082x0901000404
复制
发表时间：
2010-12
影响因子：
1
通讯作者：
Normand SL
中科院分区：
文献类型：
--
作者：
Neelon BH;O'Malley AJ;Normand SL
在涉及计数数据的应用程序中，经常会遇到过多的零。例如，在门诊服务利用的研究中，利用天数将取整数值，许多受试者没有利用（零值）。混合分布模型，如零膨胀泊松（ZIP）和零膨胀负二项（ZINB），通常用于拟合此类数据。一种更一般的混合模型，称为障碍模型，可以用来模拟零通货紧缩和零通货膨胀。几位作者提出了频率主义方法来拟合重复测量的零膨胀模型。我们描述了一个实用的贝叶斯方法，它结合了先验信息，具有最佳的小样本特性，并允许易于处理的推理。该方法可以很容易地使用标准贝叶斯软件实现。精神科门诊服务使用的研究说明了方法。
In applications involving count data, it is common to encounter an excess number of zeros. In the study of outpatient service utilization, for example, the number of utilization days will take on integer values, with many subjects having no utilization (zero values). Mixed-distribution models, such as the zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB), are often used to fit such data. A more general class of mixture models, called hurdle models, can be used to model zero-deflation as well as zero-inflation. Several authors have proposed frequentist approaches to fitting zero-inflated models for repeated measures. We describe a practical Bayesian approach which incorporates prior information, has optimal small-sample properties, and allows for tractable inference. The approach can be easily implemented using standard Bayesian software. A study of psychiatric outpatient service use illustrates the methods.