Austin Peter C, Thomas Neal, Rubin Donald B
ICES, Toronto, Ontario, Canada.
Institute of Health Management, Policy and Evaluation, University of Toronto, Toronto, Ontario, Canada.
Stat Methods Med Res. 2020 Mar;29(3):728-751. doi: 10.1177/0962280218817926. Epub 2018 Dec 20.
Matching on an estimated propensity score is frequently used to estimate the effects of treatments from observational data. Since the 1970s, different authors have proposed methods to combine matching at the design stage with regression adjustment at the analysis stage when estimating treatment effects for continuous outcomes. Previous work has consistently shown that the combination has generally superior statistical properties than either method by itself. In biomedical and epidemiological research, survival or time-to-event outcomes are common. We propose a method to combine regression adjustment and propensity score matching to estimate survival curves and hazard ratios based on estimating an imputed potential outcome under control for each successfully matched treated subject, which is accomplished using either an accelerated failure time parametric survival model or a Cox proportional hazard model that is fit to the matched control subjects. That is, a fitted model is then applied to the matched treated subjects to allow simulation of the missing potential outcome under control for each treated subject. Conventional survival analyses (e.g., estimation of survival curves and hazard ratios) can then be conducted using the observed outcome under treatment and the imputed outcome under control. We evaluated the repeated-sampling bias of the proposed methods using simulations. When using nearest neighbor matching, the proposed method resulted in decreased bias compared to crude analyses in the matched sample. We illustrate the method in an example prescribing beta-blockers at hospital discharge to patients hospitalized with heart failure.
基于估计的倾向得分进行匹配常用于从观察性数据中估计治疗效果。自20世纪70年代以来，不同的作者提出了在估计连续结果的治疗效果时，将设计阶段的匹配与分析阶段的回归调整相结合的方法。先前的研究一致表明，这种组合通常具有比单独使用任何一种方法更好的统计特性。在生物医学和流行病学研究中，生存或事件发生时间的结果很常见。我们提出了一种将回归调整和倾向得分匹配相结合的方法，基于为每个成功匹配的治疗对象估计一个在对照下的插补潜在结果来估计生存曲线和风险比，这可以使用加速失效时间参数生存模型或拟合匹配对照对象的Cox比例风险模型来实现。也就是说，然后将一个拟合模型应用于匹配的治疗对象，以允许模拟每个治疗对象在对照下缺失的潜在结果。然后可以使用治疗下的观察结果和对照下的插补结果进行传统的生存分析（例如，生存曲线和风险比的估计）。我们使用模拟评估了所提出方法的重复抽样偏差。当使用最近邻匹配时，与匹配样本中的粗略分析相比，所提出的方法导致偏差减小。我们在一个向因心力衰竭住院的患者出院时开具β受体阻滞剂的例子中说明了该方法。
