Department of Statistics, Kangwon National University, Chuncheon, Gangwon 24341, South Korea.
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, United States.
Biometrics. 2024 Jul 1;80(3). doi: 10.1093/biomtc/ujae062.
We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute.
我们使用双重嵌套病例对照（DNCC）数据的竞争风险特定比例风险模型来估计相对危险度和绝对风险（或累积发生率或粗风险）。在 DNCC 设计中，对照不仅与主要关注原因的病例时间匹配，而且还与竞争风险（第二阶段样本）的病例时间匹配。第二阶段样本中存在完整的协变量数据，但其他队列成员仅具有生存结果和一些协变量的信息。设计权重估计使用从 Samuelsen 型计算得出的用于 DNCC 的逆抽样概率进行计算。为了利用所有队列成员提供的更多信息，我们使用与竞争风险相关的项来扩展估计方程，该项对零是无偏的，但可以提高特定原因的比例风险模型的估计效率。我们建立了所提出的估计量的渐近性质，包括绝对风险的估计量，并推导出一致的方差估计量。我们表明，扩充的设计权重估计量比设计权重估计量更有效。通过模拟，我们表明在实际样本量下，所提出的渐近方法具有名义操作特性。我们使用美国国立卫生研究院前列腺癌、肺癌、结直肠癌和卵巢癌筛查试验研究中的前列腺癌死亡率数据来说明这些方法。
