Kim Sehee, Schaubel Douglas E, McCullough Keith P
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
Arbor Research Collaborative for Health, Ann Arbor, Michigan 48104, U.S.A.
Biometrics. 2018 Jun;74(2):734-743. doi: 10.1111/biom.12761. Epub 2017 Aug 3.
We propose a C-index (index of concordance) applicable to recurrent event data. The present work addresses the dearth of measures for quantifying a regression model's ability to discriminate with respect to recurrent event risk. The data which motivated the methods arise from the Dialysis Outcomes and Practice Patterns Study (DOPPS), a long-running prospective international study of end-stage renal disease patients on hemodialysis. We derive the theoretical properties of the measure under the proportional rates model (Lin et al., 2000), and propose computationally convenient inference procedures based on perturbed influence functions. The methods are shown through simulations to perform well in moderate samples. Analysis of hospitalizations among a cohort of DOPPS patients reveals substantial improvement in discrimination upon adding country indicators to a model already containing basic clinical and demographic covariates, and further improvement upon adding a relatively large set of comorbidity indicators.
我们提出了一种适用于复发事件数据的C指数（一致性指数）。目前的工作解决了量化回归模型区分复发事件风险能力的测量方法匮乏的问题。推动这些方法产生的数据来自透析结果和实践模式研究（DOPPS），这是一项针对接受血液透析的终末期肾病患者进行的长期前瞻性国际研究。我们推导了比例率模型下该测量方法的理论性质（Lin等人，2000年），并基于扰动影响函数提出了计算方便的推断程序。通过模拟表明，这些方法在中等样本量下表现良好。对一组DOPPS患者的住院情况分析显示，在一个已经包含基本临床和人口统计学协变量的模型中加入国家指标后，区分能力有显著提高，而加入一组相对较大的合并症指标后，区分能力进一步提高。
