论文标题
部分可观测时空混沌系统的无模型预测
Debiasing the estimate of treatment effect on the treated with time-varying counfounders
论文作者
论文摘要
随着大型健康数据库的可用性增加，可以评估对新数据源的治疗效果的机会。通过这些数据库，可以将时间依赖性结果分析为可以使用计数过程来衡量的事件。估计平均治疗对治疗（ATT）的效果需要建模时变的协变量和时间依赖性治疗和结果。 Gran等。提出了一种基于添加强度回归到估计ATT的易于实现的方法。我们基于Gran模型的概括来引入对ATT的依据估计，以实现可能重复的结果，并在存在多个时间依赖的协变量和基线协变量的情况下。仿真分析表明，我们校正后的估计器的表现优于Gran未校正的估计器。我们的方法应用于模仿-III数据库的重症监护现实生活数据，以估计对败血症患者的加压效应。
With the increased availability of large health databases comes the opportunity of evaluating treatment effect on new data sources.Through these databases time-dependent outcomes can be analysed as events that can be measured using counting processes. Estimating average treatment effect on the treated (ATT) requires modelling of time-varying covariate and time-dependent treatment and outcome. Gran et al. proposed an easy-to-implement method based on additive intensity regression to estimate ATT. We introduce a debiased estimate of the ATT based on a generalization of the Gran's model for a potentially repeated outcome and in the presence of multiple time-dependent covariates and baseline covariates. Simulation analyses show that our corrected estimator outperforms Gran's uncorrected estimator. Our method is applied to intensive care real-life data from MIMIC-III databases to estimate vasoppressors effect on patients with sepsis.
