Semiparametric Bayesian Analysis of Multiple Event Time Data
提出一种基于比例强度模型的贝叶斯方法分析多事件时间数据，通过引入随机脆弱项处理个体异质性，并使用吉布斯采样估计参数，以鼠肿瘤数据为例验证方法。
Abstract Multiple event time data (e.g., carcinogenic growths in different times and locations, multiple attacks of cardiac arrest) arise in various medical studies. A Bayesian analysis of such data based on proportional intensity model of multiple event time data is presented in this paper. The Bayesian structure is somewhat analogous to that used by Kalbfleisch in a proportional hazard model. An unobserved random frailty component is used in the proportional intensity model to take care of heterogeneity among the intensity processes in different subjects. The Monte Carlo method of sampling from multivariate distributions, the so-called Gibbs sampler, is used to sample from the joint posterior distribution of the unknown parameters. The methodology developed here is exemplified with the well-known data set on rat tumors of Gail, Santner, and Brown.