Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, USA.
Stat Med. 2012 Dec 30;31(30):4255-68. doi: 10.1002/sim.5511. Epub 2012 Jul 16.
In settings where a randomized trial is infeasible, observational data are frequently used to compare treatment-specific survival. The average causal effect (ACE) can be used to make inference regarding treatment policies on patient populations, and a valid ACE estimator must account for imbalances with respect to treatment-specific covariate distributions. One method through which the ACE on survival can be estimated involves appropriately averaging over Cox-regression-based fitted survival functions. A second available method balances the treatment-specific covariate distributions through inverse probability of treatment weighting and then contrasts weighted nonparametric survival function estimators. Because both methods have their advantages and disadvantages, we propose methods that essentially combine both estimators. The proposed methods are double robust, in the sense that they are consistent if at least one of the two working regression models (i.e., logistic model for treatment and Cox model for death hazard) is correct. The proposed methods involve estimating the ACE with respect to restricted mean survival time, defined as the area under the survival curve up to some prespecified time point. We derive and evaluate asymptotic results through simulation. We apply the proposed methods to estimate the ACE of donation-after-cardiac-death kidney transplantation with the use of data obtained from multiple centers in the Netherlands.
在无法进行随机试验的情况下，通常会使用观察性数据来比较特定治疗方法的生存情况。平均因果效应（ACE）可用于对患者群体的治疗政策进行推断，而有效的 ACE 估计量必须考虑到治疗特定协变量分布的不平衡。一种估计生存 ACE 的方法涉及通过适当平均基于 Cox 回归的拟合生存函数来实现。另一种可用的方法通过治疗的逆概率加权来平衡特定治疗的协变量分布，然后对比加权非参数生存函数估计量。由于这两种方法都有其优点和缺点，因此我们提出了实质上结合这两种估计量的方法。所提出的方法是双重稳健的，因为只要两个工作回归模型（即治疗的逻辑模型和死亡风险的 Cox 模型）之一是正确的，它们就是一致的。所提出的方法涉及使用受限平均生存时间（即在特定时间点之前生存曲线下的面积）来估计 ACE。我们通过模拟得出并评估了渐近结果。我们应用所提出的方法来估计荷兰多个中心获得的数据中，心死亡后肾移植的 ACE。
