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Abstract：
The case-cohort study design provides a cost-effective study design for a large cohort study with competing risk outcomes. The proportional subdistribution hazards model is widely used to estimate direct covariate effects on the cumulative incidence function for competing risk data. In biomedical studies, left truncation often occurs and brings extra challenges to the analysis. Existing inverse probability weighting methods for case-cohort studies with competing risk data not only have not addressed left truncation, but also are inefficient in regression parameter estimation for fully observed covariates. We propose an augmented inverse probability-weighted estimating equation for left-truncated competing risk data to address these limitations of the current literature. We further propose a more efficient estimator when extra information from the other causes is available. The proposed estimators are consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is unbiased and leads to estimation efficiency gain in the regression parameter estimation. We analyze the Atherosclerosis Risk in Communities study data using the proposed methods.
摘要：
病例队列研究设计为具有竞争性风险结果的大型队列研究提供了具有成本效益的研究设计。比例子分布风险模型广泛用于估计竞争风险数据对累积发生率函数的直接协变量影响。在生物医学研究中，左截断经常发生，给分析带来额外的挑战。现有的具有竞争风险数据的病例队列研究的逆概率加权方法不仅没有解决左截断问题，但对于完全观察到的协变量，回归参数估计也是低效的。我们为左截断的竞争风险数据提出了一个增强的逆概率加权估计方程，以解决当前文献的这些局限性。当来自其他原因的额外信息可用时，我们进一步提出了一种更有效的估计器。所提出的估计是一致的和渐近正态分布的。仿真研究表明，所提出的估计器是无偏的，并导致回归参数估计的估计效率增益。我们使用提出的方法分析社区研究数据中的动脉粥样硬化风险。
