Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis.
Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis.
复制标题
DOI：
10.1002/sim.7084
复制
发表时间：
2016-12-30
影响因子：
2
通讯作者：
Austin PC
中科院分区：
文献类型：
--
作者：
Austin PC
当使用观察数据估计治疗或暴露的影响时，倾向评分方法用于减少观察到的混杂效应。使用倾向评分的一种流行方法是治疗加权逆概率（IPTW）。当使用该方法时，为每名受试者计算权重，该权重等于实际接受治疗的概率的倒数。然后将这些权重纳入分析中，以尽量减少观察到的混杂效应。以前的研究发现，这些方法在估计治疗对生存结局的影响时会导致无偏估计。然而，方差估计的传统方法被证明会导致标准误的有偏估计。在本研究中，我们进行了一系列广泛的蒙特卡罗模拟，以检查使用加权考克斯比例风险模型估计治疗效果时不同的方差估计方法。我们考虑了三种方差估计方法：（i）朴素的基于模型的方差估计;（ii）稳健的三明治型方差估计;（iii）自助方差估计。我们考虑了平均治疗效果和平均治疗效果的估计。我们发现，使用自助估计导致近似正确的估计标准误差和置信区间的正确覆盖率。其他估计导致有偏估计的标准误差和置信区间不正确的覆盖率。我们的模拟是由一个案例研究，检查他汀类药物处方对死亡率的影响。© 2016作者。由John Wiley & Sons Ltd.出版。
Propensity score methods are used to reduce the effects of observed confounding when using observational data to estimate the effects of treatments or exposures. A popular method of using the propensity score is inverse probability of treatment weighting (IPTW). When using this method, a weight is calculated for each subject that is equal to the inverse of the probability of receiving the treatment that was actually received. These weights are then incorporated into the analyses to minimize the effects of observed confounding. Previous research has found that these methods result in unbiased estimation when estimating the effect of treatment on survival outcomes. However, conventional methods of variance estimation were shown to result in biased estimates of standard error. In this study, we conducted an extensive set of Monte Carlo simulations to examine different methods of variance estimation when using a weighted Cox proportional hazards model to estimate the effect of treatment. We considered three variance estimation methods: (i) a naïve model‐based variance estimator; (ii) a robust sandwich‐type variance estimator; and (iii) a bootstrap variance estimator. We considered estimation of both the average treatment effect and the average treatment effect in the treated. We found that the use of a bootstrap estimator resulted in approximately correct estimates of standard errors and confidence intervals with the correct coverage rates. The other estimators resulted in biased estimates of standard errors and confidence intervals with incorrect coverage rates. Our simulations were informed by a case study examining the effect of statin prescribing on mortality. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.