Cumulative incidence in competing risks data and competing risks regression analysis
Cumulative incidence in competing risks data and competing risks regression analysis
复制标题
DOI：
10.1158/1078-0432.ccr-06-1210
复制
发表时间：
2007-01-15
影响因子：
11.5
通讯作者：
Kim, Haesook T.
中科院分区：
文献类型：
--
作者：
Kim, Haesook T.
竞争风险在医学研究中普遍存在。例如，与治疗相关的死亡率和疾病复发都是癌症研究中令人感兴趣的重要结果和众所周知的相互竞争的风险。在竞争风险数据分析中，标准生存分析方法，如估计累积发病率的Kaplan-Meier方法、比较累积发病曲线的对数秩检验法和评估协变量的标准Cox模型等方法，会导致不正确和有偏见的结果。在本文中，我们讨论竞争风险数据分析，其中包括在存在竞争风险的情况下计算感兴趣事件的累积发生率的方法、在存在竞争风险的情况下比较累积发生率曲线的方法以及执行竞争风险回归分析的方法。通过一个假设的数字例子和真实数据，比较了竞争风险数据分析中的这三种方法与标准生存分析中各自对应的方法。卡普兰-迈耶估计的偏差的来源和大小也被详细说明。
Competing risks occur commonly in medical research. For example, both treatment-related mortality and disease recurrence are important outcomes of interest and well-known competing risks in cancer research. In the analysis of competing risks data, methods of standard survival analysis such as the Kaplan-Meier method for estimation of cumulative incidence, the log-rank test for comparison of cumulative incidence curves, and the standard Cox model for the assessment of covariates lead to incorrect and biased results. In this article, we discuss competing risks data analysis which includes methods to calculate the cumulative incidence of an event of interest in the presence of competing risks, to compare cumulative incidence curves in the presence of competing risks, and to perform competing risks regression analysis. A hypothetical numeric example and real data are used to compare those three methods in the competing risks data analysis to their respective counterparts in the standard survival analysis. The source and magnitude of bias from the Kaplan-Meier estimate is also detailed.