Guo S W, Lin D Y
Department of Biostatistics, University of Michigan, Ann Arbor 48109.
Biometrics. 1994 Sep;50(3):632-9.
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.
当每个研究对象可能经历多种类型的事件时，或者当存在观测单位的聚类，使得同一聚类内的失效时间相关时，就会出现多变量失效时间数据。失效时间通常受到区间分组的影响，或者具有真正的离散测量值。在本文中，每个离散失效时间变量的边际分布由比例风险模型的分组数据版本来制定，而相依结构未明确指定。提出了符合Liang和Zeger（1986年，《生物统计学》73卷，第13 - 22页）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且渐近正态。构建了极限协方差矩阵的稳健估计量。模拟研究表明，渐近近似在实际应用中是足够的，并且在方差 - 协方差估计中忽略聚类内的相依性会导致无效统计推断。提供了一个心理实验作为例证。
