Anstrom K J, Tsiatis A A
Department of Statistics, North Carolina State University, Raleigh 27695, USA.
Biometrics. 2001 Dec;57(4):1207-18. doi: 10.1111/j.0006-341x.2001.01207.x.
Observational studies frequently are conducted to compare long-term effects of treatments. Without randomization, patients receiving one treatment are not guaranteed to be prognostically comparable to those receiving another treatment. Furthermore, the response of interest may be right-censored because of incomplete follow-up. Statistical methods that do not account for censoring and confounding may lead to biased estimates. This article presents a method for estimating treatment effects in nonrandomized studies with right-censored responses. We review the assumptions required to estimate average causal effects and derive an estimator for comparing two treatments by applying inverse weights to the complete cases. The weights are determined according to the estimated probability of receiving treatment conditional on covariates and the estimated treatment-specific censoring distribution. By utilizing martingale representations, the estimator is shown to be asymptotically normal and an estimator for the asymptotic variance is derived. Simulation results are presented to evaluate the properties of the estimator. These methods are applied to an observational data set of acute coronary syndrome patients from Duke University Medical Center to estimate the effect of a treatment strategy on the mean 5-year medical cost.
观察性研究经常被用于比较治疗的长期效果。由于没有随机化，接受一种治疗的患者在预后上不一定与接受另一种治疗的患者具有可比性。此外，由于随访不完整，感兴趣的反应可能会被右删失。不考虑删失和混杂因素的统计方法可能会导致有偏差的估计。本文提出了一种在具有右删失反应的非随机研究中估计治疗效果的方法。我们回顾了估计平均因果效应所需的假设，并通过对完全病例应用逆权重来推导一种用于比较两种治疗的估计量。权重是根据在协变量条件下接受治疗的估计概率和估计的特定治疗删失分布来确定的。通过利用鞅表示，证明该估计量渐近正态，并推导了渐近方差的估计量。给出了模拟结果以评估该估计量的性质。这些方法被应用于杜克大学医学中心急性冠状动脉综合征患者的一个观察性数据集，以估计一种治疗策略对平均5年医疗费用的影响。
