标题： 使用动态预测和截断法计算时间依赖性暴露的人群归因分数 显示英文标题

标题： The population-attributable fraction for time-dependent exposures using dynamic prediction and landmarking

摘要： 有害暴露对公共健康的影响可以通过人群归因分数（PAF）来量化。PAF描述了由于暴露而产生的可归因风险，通常被解释为如果能够消除该暴露，可以预防的病例比例。当感兴趣的暴露依赖于时间时，PAF的定义和解释会出现困难。此时，暴露和非暴露个体的定义并不直接。我们提出动态预测和里程碑法来在这种数据情况下定义和估计PAF。讨论了两个估计量，它们基于两种可能以不同方式防止暴露的假设干预措施。考虑到第一个估计量，在每个里程碑处，估计问题被简化为一个与时间无关的情况。然后，通过使用广义线性模型来考虑当前暴露状态和进一步的（随时间变化）协变量进行简单估计。第二个估计量基于反事实结果，可以使用伪值或逆概率权重进行估计。该方法在模拟研究中进行了探索，并应用于两个数据示例。首先，我们研究了一个大型法国重症监护病房患者数据库，以估计一种病原体特异性干预措施的群体受益，该干预措施可以预防由病原体铜绿假单胞菌引起的呼吸机相关性肺炎。此外，我们量化了乳腺癌患者局部复发和远处复发的人群归因负担。 显示英文摘要

摘要： The public health impact of a harmful exposure can be quantified by the population-attributable fraction (PAF). The PAF describes the attributable risk due to an exposure and is often interpreted as the proportion of preventable cases if the exposure could be extinct. Difficulties in the definition and interpretation of the PAF arise when the exposure of interest depends on time. Then, the definition of exposed and unexposed individuals is not straightforward. We propose dynamic prediction and landmarking to define and estimate a PAF in this data situation. Two estimands are discussed which are based on two hypothetical interventions that could prevent the exposure in different ways. Considering the first estimand, at each landmark the estimation problem is reduced to a time-independent setting. Then, estimation is simply performed by using a generalized-linear model accounting for the current exposure state and further (time-varying) covariates. The second estimand is based on counterfactual outcomes, estimation can be performed using pseudo-values or inverse-probability weights. The approach is explored in a simulation study and applied on two data examples. First, we study a large French database of intensive care unit patients to estimate the population-benefit of a pathogen-specific intervention that could prevent ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa. Moreover, we quantify the population-attributable burden of locoregional and distant recurrence in breast cancer patients.