McKeague I W, Tighiouart M
Department of Statistics, Florida State University, Tallahassee, Florida 32306, USA.
Biometrics. 2000 Dec;56(4):1007-15. doi: 10.1111/j.0006-341x.2000.01007.x.
This article introduces a new Bayesian approach to the analysis of right-censored survival data. The hazard rate of interest is modeled as a product of conditionally independent stochastic processes corresponding to (1) a baseline hazard function and (2) a regression function representing the temporal influence of the covariates. These processes jump at times that form a time-homogeneous Poisson process and have a pairwise dependency structure for adjacent values. The two processes are assumed to be conditionally independent given their jump times. Features of the posterior distribution, such as the mean covariate effects and survival probabilities (conditional on the covariate), are evaluated using the Metropolis-Hastings-Green algorithm. We illustrate our methodology by an application to nasopharynx cancer survival data.
本文介绍了一种用于分析右删失生存数据的新贝叶斯方法。感兴趣的风险率被建模为条件独立随机过程的乘积，这些过程对应于：(1) 一个基线风险函数；(2) 一个表示协变量时间影响的回归函数。这些过程在构成齐次泊松过程的时间点上跳跃，并且相邻值具有成对依赖结构。假设这两个过程在给定其跳跃时间的情况下是条件独立的。使用Metropolis-Hastings-Green算法评估后验分布的特征，如平均协变量效应和生存概率（以协变量为条件）。我们通过将其应用于鼻咽癌生存数据来说明我们的方法。
