REGRESSION-ANALYSIS OF MULTIVARIATE GROUPED SURVIVAL-DATA
REGRESSION-ANALYSIS OF MULTIVARIATE GROUPED SURVIVAL-DATA
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DOI：
10.2307/2532778
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发表时间：
1994-09-01
期刊：
影响因子：
1.9
通讯作者：
LIN, DY
中科院分区：
文献类型：
--
作者：
GUO, SW;LIN, DY
当每个研究主题可能会经历几种类型的事件或观察单元的聚类以使同一群集内的故障时间相关时，就会出现多元故障时间数据。故障时间通常会进行间隔分组或具有真正离散的测量。在本文中，每个离散故障时间变量的边际分布由比例危害模型的分组数据配制，而依赖性结构未指定。提出了Liang and Zeger（1986，Biometrika 73，13-22）精神的广义估计方程，以估计回归参数和生存概率。所得估计量是一致的，并且渐近地正常。构建了限制协方差矩阵的稳健估计器。仿真研究表明，渐近近似足以用于实际使用，并且忽略群内依赖性在方差 - 协方差估计中的依赖性将导致无效的统计推断。提供了一个心理实验供插图。
Multivariate failure time data arise when each study subject may experience several types of event or when there are clusterings of observational units such that failure times within the same cluster are correlated. The failure times are often subject to interval grouping or have truly discrete measurements. In this paper, the marginal distribution for each discrete failure time variable is formulated by a grouped-data version of the proportional hazards model while the dependence structure is unspecified. Generalized estimating equations in the spirit of Liang and Zeger (1986, Biometrika 73, 13-22) are proposed to estimate the regression parameters and survival probabilities. The resulting estimators are consistent and asymptotically normal. Robust estimators for the limiting covariance matrices are constructed. Simulation studies demonstrate that the asymptotic approximations are adequate for practical use and that ignoring the intracluster dependence in the variance-covariance estimation would lead to invalid statistical inference. A psychological experiment is provided for illustration.