Ertefaie Ashkan, Stephens David A
McGill University, Canada.
Int J Biostat. 2010;6(2):Article 14. doi: 10.2202/1557-4679.1198.
In observational studies for causal effects, treatments are assigned to experimental units without the benefits of randomization. As a result, there is the potential for bias in the estimation of the treatment effect. Two methods for estimating the causal effect consistently are Inverse Probability of Treatment Weighting (IPTW) and the Propensity Score (PS). We demonstrate that in many simple cases, the PS method routinely produces estimators with lower Mean-Square Error (MSE). In the longitudinal setting, estimation of the causal effect of a time-dependent exposure in the presence of time-dependent covariates that are themselves affected by previous treatment also requires adjustment approaches. We describe an alternative approach to the classical binary treatment propensity score termed the Generalized Propensity Score (GPS). Previously, the GPS has mainly been applied in a single interval setting; we use an extension of the GPS approach to the longitudinal setting. We compare the strengths and weaknesses of IPTW and GPS for causal inference in three simulation studies and two real data sets. Again, in simulation, the GPS appears to produce estimators with lower MSE.
在针对因果效应的观察性研究中，治疗措施被分配给实验单位，却没有随机化带来的优势。因此，在治疗效果的估计中存在偏差的可能性。两种一致估计因果效应的方法是治疗权重逆概率法（IPTW）和倾向得分法（PS）。我们证明，在许多简单情况下，PS方法通常会产生均方误差（MSE）较低的估计量。在纵向研究中，在存在本身受先前治疗影响的随时间变化的协变量的情况下，估计随时间变化的暴露的因果效应也需要调整方法。我们描述了一种替代经典二元治疗倾向得分的方法，称为广义倾向得分（GPS）。以前，GPS主要应用于单个区间设置；我们将GPS方法扩展到纵向研究中。我们在三项模拟研究和两个真实数据集上比较了IPTW和GPS在因果推断方面的优缺点。同样，在模拟中，GPS似乎能产生MSE较低的估计量。
