Utilizing propensity scores to estimate causal treatment effects with censored time-lagged data
Utilizing propensity scores to estimate causal treatment effects with censored time-lagged data
复制标题
DOI：
10.1111/j.0006-341x.2001.01207.x
复制
发表时间：
2001-12-01
期刊：
影响因子：
1.9
通讯作者：
Tsiatis, AA
中科院分区：
文献类型：
--
作者：
Anstrom, KJ;Tsiatis, AA
经常进行观察性研究来比较治疗的长期效果。如果没有随机化，接受一种治疗的患者不能保证预后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，感兴趣的回应可能受到权利审查。不考虑审查和混淆的统计方法可能导致有偏差的估计。本文提出了一种估计具有右删节反应的非随机研究中治疗效果的方法。我们回顾了估计平均因果效应所需的假设，并通过对完整病例应用逆权重推导出比较两种处理的估计量。权重根据协变量条件下接受治疗的估计概率和估计的治疗特异性审查分布来确定。利用鞅表示，证明了估计量是渐近正态的，并给出了渐近方差的估计量。仿真结果验证了该估计器的性能。这些方法应用于杜克大学医学中心急性冠脉综合征患者的观察性数据集，以估计治疗策略对平均5年医疗费用的影响。
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 axe 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.