Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N Martin, PO Box 245211, Tucson, AZ 85724-5211, U.S.A.
Stat Med. 2010 Sep 20;29(21):2215-23. doi: 10.1002/sim.3969.
The weighted Kaplan-Meier (WKM) estimator is often used to incorporate prognostic covariates into survival analysis to improve efficiency and correct for potential bias. In this paper, we generalize the WKM estimator to handle a situation with multiple prognostic covariates and potential-dependent censoring through the use of prognostic covariates. We propose to combine multiple prognostic covariates into two risk scores derived from two working proportional hazards models. One model is for the event times. The other model is for the censoring times. These two risk scores are then categorized to define the risk groups needed for the WKM estimator. A method of defining categories based on principal components is proposed. We show that the WKM estimator is robust to misspecification of either one of the two working models. In simulation studies, we show that the robust WKM approach can reduce bias due to dependent censoring and improve efficiency. We apply the robust WKM approach to a prostate cancer data set.
加权 Kaplan-Meier（WKM）估计器常用于将预后协变量纳入生存分析中，以提高效率并纠正潜在偏差。在本文中，我们通过使用预后协变量将 WKM 估计器推广到处理具有多个预后协变量和潜在依赖性删失的情况。我们建议将多个预后协变量组合成两个源自两个工作比例风险模型的风险评分。一个模型用于事件时间，另一个模型用于删失时间。然后，这两个风险评分被分类以定义 WKM 估计器所需的风险组。提出了一种基于主成分的分类方法。我们表明，WKM 估计器对两个工作模型之一的误指定具有稳健性。在模拟研究中，我们表明，稳健的 WKM 方法可以减少由于依赖删失引起的偏差并提高效率。我们将稳健的 WKM 方法应用于前列腺癌数据集。
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