Liu Lei, Ma Jennie Z, O'Quigley John
Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908-0717, U.S.A.
Stat Med. 2008 Nov 29;27(27):5679-91. doi: 10.1002/sim.3392.
Shared random effects models have been increasingly common in the joint analyses of repeated measures (e.g. CD4 counts, hemoglobin levels) and a correlated failure time such as death. In this paper we study several shared random effects models in the multi-level repeated measures data setting with dependent failure times. Distinct random effects are used to characterize heterogeneity in repeated measures at different levels. The hazard of death may be dependent on random effects from various levels. To simplify the estimation procedure, we adopt the Gaussian quadrature technique with a piecewise log-linear baseline hazard for the death process, which can be conveniently implemented in the freely available software aML. As an example, we analyze repeated measures of hematocrit level and survival for end stage renal disease patients clustered within a randomly selected 126 dialysis centers in the U.S. renal data system data set. Our model is very comprehensive yet easy to implement, making it appealing to general statistical practitioners.
共享随机效应模型在重复测量（如CD4计数、血红蛋白水平）与相关失效时间（如死亡）的联合分析中越来越常见。在本文中，我们研究了在具有相依失效时间的多层次重复测量数据设置中的几种共享随机效应模型。不同的随机效应用于刻画不同层次重复测量中的异质性。死亡风险可能依赖于各个层次的随机效应。为简化估计过程，我们采用高斯求积技术，并对死亡过程采用分段对数线性基线风险，这可以在免费软件aML中方便地实现。作为一个例子，我们分析了美国肾脏数据系统数据集中随机选择的126个透析中心内终末期肾病患者的血细胞比容水平重复测量值和生存率。我们的模型非常全面且易于实现，对一般统计从业者具有吸引力。
