Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA.
Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA.
Stat Med. 2018 Feb 10;37(3):390-404. doi: 10.1002/sim.7513. Epub 2017 Oct 10.
In many medical studies, estimation of the association between treatment and outcome of interest is often of primary scientific interest. Standard methods for its evaluation in survival analysis typically require the assumption of independent censoring. This assumption might be invalid in many medical studies, where the presence of dependent censoring leads to difficulties in analyzing covariate effects on disease outcomes. This data structure is called "semicompeting risks data," for which many authors have proposed an artificial censoring technique. However, confounders with large variability may lead to excessive artificial censoring, which subsequently results in numerically unstable estimation. In this paper, we propose a strategy for weighted estimation of the associations in the accelerated failure time model. Weights are based on propensity score modeling of the treatment conditional on confounder variables. This novel application of propensity scores avoids excess artificial censoring caused by the confounders and simplifies computation. Monte Carlo simulation studies and application to AIDS and cancer research are used to illustrate the methodology.
在许多医学研究中，治疗与感兴趣的结果之间的关联估计通常是主要的科学关注点。在生存分析中评估其的标准方法通常需要独立删失的假设。在许多医学研究中，这种假设可能不成立，因为存在相关删失会导致分析协变量对疾病结果的影响变得困难。这种数据结构被称为“半竞争风险数据”，许多作者为此提出了一种人为删失技术。然而，具有较大变异性的混杂因素可能导致过度的人为删失，从而导致数值不稳定的估计。在本文中，我们提出了一种在加速失效时间模型中加权估计关联的策略。权重基于混杂变量条件下的治疗倾向得分建模。这种倾向得分的新应用避免了混杂因素引起的过度人为删失，并简化了计算。通过蒙特卡罗模拟研究和艾滋病与癌症研究的应用来说明该方法。
