COX REGRESSION-ANALYSIS OF MULTIVARIATE FAILURE TIME DATA - THE MARGINAL APPROACH
COX REGRESSION-ANALYSIS OF MULTIVARIATE FAILURE TIME DATA - THE MARGINAL APPROACH
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DOI：
10.1002/sim.4780132105
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发表时间：
1994-11-15
影响因子：
2
通讯作者：
LIN, DY
中科院分区：
文献类型：
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作者：
LIN, DY
多变量失效时间数据在科学研究中经常遇到，因为每个研究对象可能经历多个事件，或者因为存在受试者聚类，使得同一聚类内的失效时间相关。在本文中，我提出了一个一般的方法来分析这些数据，这是类似于梁和Zeger的纵向数据分析。这种方法用熟悉的考克斯比例风险模型来描述多变量失效时间的边际分布，而相关失效时间之间的依赖性完全不确定。边际模型的基线风险函数可以相同或不同。简单的回归参数估计方程的开发，产生一致的和渐近正态的估计，和强大的方差-协方差估计构建占类内相关。仿真结果表明，大样本近似是足够的实际使用，忽略类内相关性可能会产生相当误导的方差估计。所提出的方法已完全实施在一个简单的计算机程序，其中还包括几种替代方法。提供了来自四项临床或流行病学研究的数据的详细说明。
Multivariate failure time data are commonly encountered in scientific investigations because each study subject may experience multiple events or because there exists clustering of subjects such that failure times within the same cluster are correlated. In this paper, I present a general methodology for analysing such data, which is analogous to that of Liang and Zeger for longitudinal data analysis. This approach formulates the marginal distributions of multivariate failure times with the familiar Cox proportional hazards models while leaving the nature of dependence among related failure times completely unspecified. The baseline hazard functions for the marginal models may be identical or different. Simple estimating equations for the regression parameters are developed which yield consistent and asymptotically normal estimators, and robust variance-covariance estimators are constructed to account for the intra-class correlation. Simulation results demonstrate that the large-sample approximations are adequate for practical use and that ignoring the intra-class correlation could yield rather misleading variance estimators. The proposed methodology has been fully implemented in a simple computer program which also incorporates several alternative approaches. Detailed illustrations with data from four clinical or epidemiologic studies are provided.