Nan Bin, Wellner Jon A
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029.
Department of Statistics, University of Washington, Seattle, WA 98195-4322.
Stat Sin. 2013 Jul 1;23(3):1155-1180.
Case-cohort design, an outcome-dependent sampling design for censored survival data, is increasingly used in biomedical research. The development of asymptotic theory for a case-cohort design in the current literature primarily relies on counting process stochastic integrals. Such an approach, however, is rather limited and lacks theoretical justification for outcome-dependent weighted methods due to non-predictability. Instead of stochastic integrals, we derive asymptotic properties for case-cohort studies based on a general Z-estimation theory for semi-parametric models with bundled parameters using empirical process theory. Both the Cox model and the additive hazards model with time-dependent covariates are considered.
病例队列设计是一种用于删失生存数据的基于结果的抽样设计，在生物医学研究中越来越多地被使用。当前文献中病例队列设计的渐近理论发展主要依赖于计数过程随机积分。然而，由于不可预测性，这种方法相当有限，并且缺乏基于结果的加权方法的理论依据。我们基于使用经验过程理论的具有捆绑参数的半参数模型的一般Z估计理论，推导病例队列研究的渐近性质，而不是使用随机积分。同时考虑了Cox模型和具有时间相依协变量的加法风险模型。
