时间依赖性暴露和竞争风险的人群归因分数——对估计量的讨论。

The population-attributable fraction for time-dependent exposures and competing risks-A discussion on estimands.

机构信息

Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.

Freiburg Center for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany.

出版信息

Stat Med. 2019 Sep 10;38(20):3880-3895. doi: 10.1002/sim.8208. Epub 2019 Jun 4.

DOI:10.1002/sim.8208

PMID:31162706

Abstract

The population-attributable fraction (PAF) quantifies the public health impact of a harmful exposure. Despite being a measure of significant importance, an estimand accommodating complicated time-to-event data is not clearly defined. We discuss current estimands of the PAF used to quantify the public health impact of an internal time-dependent exposure for data subject to competing outcomes. To overcome some limitations, we proposed a novel estimand that is based on dynamic prediction by landmarking. In a profound simulation study, we discuss interpretation and performance of the various estimands and their estimators. The methods are applied to a large French database to estimate the health impact of ventilator-associated pneumonia for patients in intensive care.

摘要

人群归因分数（PAF）定量评估有害暴露对公共卫生的影响。尽管它是一个非常重要的衡量指标，但目前还没有明确定义适用于处理复杂时间事件数据的评估量。我们讨论了目前用于量化内部时变暴露对竞争结果数据的公共卫生影响的 PAF 评估量。为了克服一些局限性，我们提出了一种新的评估量，该评估量基于通过定标进行的动态预测。在一项深入的模拟研究中，我们讨论了各种评估量及其估计量的解释和性能。该方法应用于一个大型的法国数据库，以估计重症监护患者呼吸机相关性肺炎的健康影响。