Liao J G
Department of Epidemiology and Biostatistics, University of South Florida, Tampa 33612, USA.
Biometrics. 1999 Mar;55(1):268-72. doi: 10.1111/j.0006-341x.1999.00268.x.
This paper introduces a hierarchical Bayesian model for combining multiple 2 x 2 tables that allows the flexibility of different odds ratio estimates for different tables and at the same time allows the tables to borrow information from each other. The proposed model, however, is different from a full Bayesian model in that the nuisance parameters are eliminated by conditioning instead of integration. The motivation is a more robust model and a faster and more stable Gibbs algorithm. We work out a Gibbs scheme using the adaptive rejection sampling for log concave density and an algorithm for the mean and variance of the noncentral hypergeometric distribution. The model is applied to a multicenter ulcer clinical trial.
本文介绍了一种用于合并多个2×2列联表的分层贝叶斯模型，该模型允许对不同的表格采用不同的优势比估计，同时允许各表格之间相互借鉴信息。然而，所提出的模型与完全贝叶斯模型不同，因为干扰参数是通过条件设定而非积分来消除的。这样做的动机是为了得到一个更稳健的模型以及一个更快且更稳定的吉布斯算法。我们利用针对对数凹密度的自适应拒绝抽样方法制定了一种吉布斯方案，并给出了一个用于计算非中心超几何分布均值和方差的算法。该模型被应用于一项多中心溃疡临床试验。
