Bayesian Proportional Odds Models for Analyzing Current Status Data: Univariate, Clustered, and Multivariate
Bayesian Proportional Odds Models for Analyzing Current Status Data: Univariate, Clustered, and Multivariate
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DOI：
10.1080/03610918.2011.566971
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发表时间：
2011-01-01
影响因子：
0.9
通讯作者：
Wang, Lianming
中科院分区：
文献类型：
--
作者：
Lin, Xiaoyan;Wang, Lianming
目前的状态数据通常出现在许多领域，如流行病学研究和横断面致瘤性研究。在这篇文章中，我们提出了一个半参数贝叶斯方法分析当前状态数据的比例优势模型。使用单调样条的基线优势函数和一种新的数据增强泊松潜变量，使简单的更新后验计算中的所有参数。所提出的方法表现出良好的性能，并在仿真研究中与Wang和Dunson（2010）的方法进行了比较。我们还推广了所提出的方法来分析集群和多变量的当前状态数据下的脆弱比例优势模型。
Current status data commonly arise in many fields such as epidemiological studies and cross-sectional tumorigenicity studies. In this article, we propose a semiparametric Bayesian approach for analyzing current status data with the proportional odds model. The use of monotone splines for the baseline odds function and a novel data augmentation with Poisson latent variables enable simple updating all of the parameters in the posterior computation. The proposed approach shows good performance and is compared with the approach in Wang and Dunson (2010) in a simulation study. We also generalize the proposed approach to analyze clustered and multivariate current status data under the frailty proportional odds models.