A generalized case-cohort design has been used when measuring exposures is expensive and events are not rare in the full cohort. This design collects expensive exposure information from a (stratified) randomly selected subset from the full cohort, called the subcohort, and a fraction of cases outside the subcohort. For the full cohort study with competing risks, He et al. (Scand J Stat 43:103-122, 2016) studied the non-stratified proportional subdistribution hazards model with covariate-dependent censoring to directly evaluate covariate effects on the cumulative incidence function. In this paper, we propose a stratified proportional subdistribution hazards model with covariate-adjusted censoring weights for competing risks data under the generalized case-cohort design. We consider a general class of weight functions to account for the generalized case-cohort design. Then, we derive the optimal weight function which minimizes the asymptotic variance of parameter estimates within the general class of weight functions. The proposed estimator is shown to be consistent and asymptotically normally distributed. The simulation studies show (i) the proposed estimator with covariate-adjusted weight is unbiased when the censoring distribution depends on covariates; and (ii) the proposed estimator with the optimal weight function gains parameter estimation efficiency. We apply the proposed method to stem cell transplantation and diabetes data sets.

当测量暴露费用昂贵且事件在整个队列中并不少见时，已使用广义病例队列设计。该设计从整个队列（称为子队列）中随机选择的（分层）子集和子队列外的一小部分病例中收集昂贵的暴露信息。对于具有竞争风险的完整队列研究，He 等人。(Scand J Stat 43:103-122, 2016) 研究了具有协变量相关审查的非分层比例子分布风险模型，以直接评估协变量对累积发生率函数的影响。在本文中，我们针对广义病例队列设计下的竞争风险数据提出了一个具有协变量调整删失权重的分层比例子分布风险模型。我们考虑一类通用的权重函数来解释广义病例队列设计。然后，我们推导出最优权重函数，它在一般权重函数类中最小化参数估计的渐近方差。所提出的估计量被证明是一致的和渐近正态分布的。模拟研究表明（i）当审查分布取决于协变量时，所提出的具有协变量调整权重的估计量是无偏的；(ii) 具有最佳权重函数的建议估计器获得参数估计效率。我们将所提出的方法应用于干细胞移植和糖尿病数据集。所提出的估计量被证明是一致的和渐近正态分布的。模拟研究表明（i）当审查分布取决于协变量时，所提出的具有协变量调整权重的估计量是无偏的；(ii) 具有最佳权重函数的建议估计器获得参数估计效率。我们将所提出的方法应用于干细胞移植和糖尿病数据集。所提出的估计量被证明是一致的和渐近正态分布的。模拟研究表明（i）当审查分布取决于协变量时，所提出的具有协变量调整权重的估计量是无偏的；(ii) 具有最佳权重函数的建议估计器获得参数估计效率。我们将所提出的方法应用于干细胞移植和糖尿病数据集。