Department of Biostatistics and Data Science, School of Public Health, The University of Texas, Houston, TX, USA.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Stat Med. 2018 Nov 20;37(26):3745-3763. doi: 10.1002/sim.7839. Epub 2018 May 31.
Propensity score analysis is widely used in observational studies to adjust for confounding and estimate the causal effect of a treatment on the outcome. When the outcome is survival time, there are special considerations on the definition of the causal estimand, point, and variance estimation that have not been thoroughly studied in the literature. We investigate propensity score analysis of survival data with a class of weighting methods. We consider the following estimands in the two-sample context: average survival time, restricted average survival time, survival probability, survival quantile, and the marginal hazard ratio. We propose a unified analytic framework to obtain the point and variance estimators. The proposed methodology properly adjusts for the sampling variability in the estimated propensity scores. Extensive simulations show that the point and variance estimators possess desired finite sample properties and demonstrate better numerical performance than some existing weighting and matching methods commonly used in the literature. The proposed methodology is illustrated with data from a breast cancer study.
倾向评分分析广泛应用于观察性研究中，以调整混杂因素并估计治疗对结局的因果效应。当结局是生存时间时，在因果估计量、点估计和方差估计的定义方面，文献中尚未进行彻底研究。我们研究了一类加权方法对生存数据的倾向评分分析。在两样本情况下，我们考虑以下估计量：平均生存时间、受限平均生存时间、生存概率、生存分位数和边缘危险比。我们提出了一个统一的分析框架来获得点估计和方差估计。所提出的方法适当地调整了估计倾向得分的抽样变异性。广泛的模拟表明，点估计和方差估计具有理想的有限样本性质，并表现出比文献中常用的一些现有加权和匹配方法更好的数值性能。该方法通过乳腺癌研究的数据进行了说明。
