Ghosh Debashis, Lin D Y
Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, Michigan, USA.
Biometrics. 2003 Dec;59(4):877-85. doi: 10.1111/j.0006-341x.2003.00102.x.
Dependent censoring occurs in longitudinal studies of recurrent events when the censoring time depends on the potentially unobserved recurrent event times. To perform regression analysis in this setting, we propose a semiparametric joint model that formulates the marginal distributions of the recurrent event process and dependent censoring time through scale-change models, while leaving the distributional form and dependence structure unspecified. We derive consistent and asymptotically normal estimators for the regression parameters. We also develop graphical and numerical methods for assessing the adequacy of the proposed model. The finite-sample behavior of the new inference procedures is evaluated through simulation studies. An application to recurrent hospitalization data taken from a study of intravenous drug users is provided.
当删失时间取决于潜在未观察到的复发事件时间时，相依删失会出现在复发事件的纵向研究中。为了在这种情况下进行回归分析，我们提出了一种半参数联合模型，该模型通过尺度变换模型来构建复发事件过程和相依删失时间的边际分布，同时不指定分布形式和相依结构。我们推导出回归参数的一致且渐近正态的估计量。我们还开发了用于评估所提模型充分性的图形和数值方法。通过模拟研究评估了新推断程序的有限样本行为。提供了一个应用于来自静脉吸毒者研究的复发住院数据的实例。
