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Abstract：
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.
摘要：
人群归因分数（PAF）被广泛用于量化与人群中可修改的暴露相关的疾病负担。它已扩展到随时间变化的测量，为队列研究提供了关于暴露的影响何时以及如何随时间变化的额外信息。然而,没有使用从基于人群的病例对照研究中收集的数据对PAF进行估计的程序，which,因为时间和成本效率，通常用于研究疾病发病率的遗传和环境危险因素。在这篇文章中,我们表明，时变PAF可以从病例对照研究中识别出来，并开发了一种新的PAF估计器。在内核平滑器的情况下，我们的估计器结合了逻辑回归模型的赔率比估计和以故障时间为条件的风险因子分布的密度估计。所提出的估计器被证明是一致的且渐近正态的，其渐近方差可以根据经验从数据中估计。仿真研究表明，所提出的估计器在有限的样本量下表现良好。最后,以人群为基础的结直肠癌病例对照研究说明了该方法.
