Zhao Wei, Chen Ying Qing, Hsu Li
Department of Biostatistics, University of Washington, Seattle, Washington, U.S.A.
Biostatistics and Biomathematics Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.
Biometrics. 2017 Sep;73(3):866-875. doi: 10.1111/biom.12648. Epub 2017 Jan 18.
Population attributable fraction (PAF) is widely used to quantify the disease burden associated with a modifiable exposure in a population. It has been extended to a time-varying measure that provides additional information on when and how the exposure's impact varies over time for cohort studies. However, there is no estimation procedure for PAF using data that are collected from population-based case-control studies, which, because of time and cost efficiency, are commonly used for studying genetic and environmental risk factors of disease incidences. In this article, we show that time-varying PAF is identifiable from a case-control study and develop a novel estimator of PAF. Our estimator combines odds ratio estimates from logistic regression models and density estimates of the risk factor distribution conditional on failure times in cases from a kernel smoother. The proposed estimator is shown to be consistent and asymptotically normal with asymptotic variance that can be estimated empirically from the data. Simulation studies demonstrate that the proposed estimator performs well in finite sample sizes. Finally, the method is illustrated by a population-based case-control study of colorectal cancer.
人群归因分数（PAF）被广泛用于量化人群中与可改变暴露因素相关的疾病负担。它已扩展为一种随时间变化的度量方法，可为队列研究提供关于暴露影响随时间变化的时间和方式的额外信息。然而，对于基于人群的病例对照研究收集的数据，尚无PAF的估计程序，而基于人群的病例对照研究由于时间和成本效率方面的优势，常用于研究疾病发病率的遗传和环境风险因素。在本文中，我们表明病例对照研究中可以识别随时间变化的PAF，并开发了一种新的PAF估计方法。我们的估计方法结合了逻辑回归模型的比值比估计和基于核平滑器的病例中失败时间条件下风险因素分布的密度估计。所提出的估计方法被证明是一致的，并且渐近正态，其渐近方差可以从数据中通过经验估计。模拟研究表明，所提出的估计方法在有限样本量下表现良好。最后，通过一项基于人群的结直肠癌病例对照研究对该方法进行了说明。
