Department of Biostatistics & Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Biometrics. 2023 Dec;79(4):3010-3022. doi: 10.1111/biom.13821. Epub 2023 Jan 31.
In survival data analysis, a competing risk is an event whose occurrence precludes or alters the chance of the occurrence of the primary event of interest. In large cohort studies with long-term follow-up, there are often competing risks. Further, if the event of interest is rare in such large studies, the case-cohort study design is widely used to reduce the cost and achieve the same efficiency as a cohort study. The conventional additive hazards modeling for competing risks data in case-cohort studies involves the cause-specific hazard function, under which direct assessment of covariate effects on the cumulative incidence function, or the subdistribution, is not possible. In this paper, we consider an additive hazard model for the subdistribution of a competing risk in case-cohort studies. We propose estimating equations based on inverse probability weighting methods for the estimation of the model parameters. Consistency and asymptotic normality of the proposed estimators are established. The performance of the proposed methods in finite samples is examined through simulation studies and the proposed approach is applied to a case-cohort dataset from the Sister Study.
在生存数据分析中，竞争风险是指一种事件的发生排除或改变了主要关注事件发生的机会。在具有长期随访的大型队列研究中，通常存在竞争风险。此外，如果此类大型研究中感兴趣的事件很少，则广泛使用病例-队列研究设计来降低成本并实现与队列研究相同的效率。病例-队列研究中竞争风险数据的传统加性风险模型涉及特定原因的风险函数，在这种情况下，无法直接评估协变量对累积发生率函数（或子分布）的影响。在本文中，我们考虑了病例-队列研究中竞争风险的子分布的加性风险模型。我们提出了基于逆概率加权方法的估计方程，用于模型参数的估计。建立了所提出估计量的一致性和渐近正态性。通过模拟研究检验了所提出方法在有限样本中的性能，并将所提出的方法应用于来自 Sister 研究的病例-队列数据集。
