Dunson David B, Dinse Gregg E
Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, USA.
Biometrics. 2002 Mar;58(1):79-88. doi: 10.1111/j.0006-341x.2002.00079.x.
Multivariate current status data, consist of indicators of whether each of several events occur by the time of a single examination. Our interest focuses on inferences about the joint distribution of the event times. Conventional methods for analysis of multiple event-time data cannot be used because all of the event times are censored and censoring may be informative. Within a given subject, we account for correlated event times through a subject-specific latent variable, conditional upon which the various events are assumed to occur independently. We also assume that each event contributes independently to the hazard of censoring. Nonparametric step functions are used to characterize the baseline distributions of the different event times and of the examination times. Covariate and subject-specific effects are incorporated through generalized linear models. A Markov chain Monte Carlo algorithm is described for estimation of the posterior distributions of the unknowns. The methods are illustrated through application to multiple tumor site data from an animal carcinogenicity study.
多变量当前状态数据由在单次检查时几个事件中每个事件是否发生的指标组成。我们的兴趣集中在对事件时间联合分布的推断上。由于所有事件时间都被截尾且截尾可能是信息性的，所以不能使用分析多个事件时间数据的传统方法。在给定个体内，我们通过特定个体的潜在变量来考虑相关的事件时间，在此条件下，假设各种事件独立发生。我们还假设每个事件对截尾风险有独立贡献。使用非参数阶梯函数来刻画不同事件时间和检查时间的基线分布。协变量和特定个体效应通过广义线性模型纳入。描述了一种马尔可夫链蒙特卡罗算法用于估计未知量的后验分布。通过应用于动物致癌性研究中的多个肿瘤部位数据来说明这些方法。
