Estimating the Treatment Effect on the Treated Under Time-Dependent Confounding in an Application to the Swiss HIV Cohort Study
提出一种基于加性风险回归和线性增量模型的两步法，用于在非随机化研究中校正时间依赖性混杂，估计处理组的平均处理效应，并以瑞士HIV队列研究为例，评估抗逆转录病毒治疗对艾滋病或死亡时间的影响。
Summary When comparing time varying treatments in a non-randomized setting, one must often correct for time-dependent confounders that influence treatment choice over time and that are themselves influenced by treatment. We present a new two-step procedure, based on additive hazard regression and linear increments models, for handling such confounding when estimating average treatment effects on the treated. The approach can also be used for mediation analysis. The method is applied to data from the Swiss HIV Cohort Study, estimating the effect of antiretroviral treatment on time to acquired immune deficiency syndrome or death. Compared with other methods for estimating the average treatment effects on the treated the method proposed is easy to implement by using available software packages in R.