Estimating the Causal Effect of Treatment in Observational Studies with Survival Time End Points and Unmeasured Confounding
针对观察性研究中治疗对生存时间（可能被删失）的因果效应，提出一种联立方程模型，利用工具变量和双变量分布假设来处理未测量的混杂因素，并在腹主动脉瘤破裂修复数据上展示方法。
Estimation of the effect of a treatment in the presence of unmeasured confounding is a common objective in observational studies. The Two Stage Least Squares (2SLS) Instrumental Variables (IV) procedure is frequently used but is not applicable to time-to-event data if some observations are censored. We develop a simultaneous equations model (SEM) to account for unmeasured confounding of the effect of treatment on survival time subject to censoring. The identification of the treatment effect is assisted by IVs (variables related to treatment but conditional on treatment not to the outcome) and the assumed bivariate distribution underlying the data generating process. The methodology is illustrated on data from an observational study of time to death following endovascular or open repair of ruptured abdominal aortic aneurysm. As the IV and the distributional assumptions cannot be jointly assessed from the observed data, we evaluate the sensitivity of the results to these assumptions.