Department of Biostatistics, University of Washington, Seattle, WA, USA.
Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Stat Methods Med Res. 2020 Jan;29(1):243-257. doi: 10.1177/0962280219831725. Epub 2019 Feb 25.
Population attributable fraction is a widely used measure for quantifying the disease burden associated with a modifiable exposure of interest at the population level. It has been extended to a time-varying measure, population attributable hazard function, to provide additional information on when and how the exposure's impact varies over time. However, like the classic population attributable fraction, the population attributable hazard is generally biased if confounders are present. In this article, we provide a natural definition of adjusted population attributable hazard to take into account the effects of confounders, and its alternative that is identifiable from case-control studies under the rare disease assumption. We propose a novel estimator, which combines the odds ratio estimator from logistic regression model, and the conditional density function estimator of the exposure and confounding variables distribution given the failure times of cases or the current times of controls from a kernel smoother. We show that the proposed estimators are consistent and asymptotically normal with variance that can be estimated empirically from the data. Simulation studies demonstrate that the proposed estimators perform well in finite sample sizes. Finally, we illustrate the method by an analysis of a case-control study of colorectal cancer. Supplementary materials for this article are available online.
人群归因分数是一种广泛用于量化与可改变的暴露相关的疾病负担的指标，适用于人群水平。它已经扩展为一个时变的指标，即人群归因危险函数，以提供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，与经典的人群归因分数一样，如果存在混杂因素，人群归因危险通常会存在偏差。在本文中，我们提供了一种调整后的人群归因危险的自然定义，以考虑混杂因素的影响，以及在罕见疾病假设下，可从病例对照研究中识别的替代定义。我们提出了一种新的估计量，它结合了来自逻辑回归模型的优势比估计量，以及来自核平滑器的暴露和混杂变量分布的条件密度函数估计量，给定病例的失效时间或对照的当前时间。我们证明了所提出的估计量在有限样本大小下是一致的和渐近正态的，并且可以从数据中经验估计出方差。模拟研究表明，所提出的估计量在有限样本大小下表现良好。最后，我们通过对结直肠癌的病例对照研究进行分析来说明该方法。本文的补充材料可在线获取。
