Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA.
Children's Oncology Group, Monrovia, California, USA.
Stat Med. 2021 Dec 30;40(30):6885-6899. doi: 10.1002/sim.9216. Epub 2021 Oct 17.
Time-dependent receiver operating characteristic curves are often used to evaluate the classification performance of continuous measures when considering time-to-event data. When one is interested in evaluating the predictive performance of multiple covariates, it is common to use the Cox proportional hazards model to obtain risk scores; however, previous work has shown that when the model is mis-specified, the estimand corresponding to the partial likelihood estimator depends on the censoring distribution. In this manuscript, we show that when the risk score model is mis-specified, the AUC will also depend on the censoring distribution, leading to either over- or under-estimation of the risk score's predictive performance. We propose the use of censoring-robust estimators to remove the dependence on the censoring distribution and provide empirical results supporting the use of censoring-robust risk scores.
时间依赖性受试者工作特征曲线常用于评估考虑事件时间数据时连续测量的分类性能。当人们有兴趣评估多个协变量的预测性能时，通常使用 Cox 比例风险模型来获得风险评分；然而，之前的研究表明，当模型被错误指定时，部分似然估计量对应的估计量取决于删失分布。在本文中，我们表明，当风险评分模型被错误指定时，AUC 也将取决于删失分布，从而导致对风险评分预测性能的高估或低估。我们建议使用删失稳健估计量来消除对删失分布的依赖，并提供支持使用删失稳健风险评分的实证结果。
