Klein John P
Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA.
Stat Med. 2006 Mar 30;25(6):1015-34. doi: 10.1002/sim.2246.
Competing risks arise commonly in the analysis of cancer studies. Most common are the competing risks of relapse and death in remission. These two risks are the primary reason that patients fail treatment. In most medical papers the effects of covariates on the three outcomes (relapse, death in remission and treatment failure) are model by distinct proportional hazards regression models. Since the hazards of relapse and death in remission must add to that of treatment failure, we argue that this model leads to internal inconsistencies. We argue that additive models for either the hazard rates or the cumulative incidence functions are more natural and that these models properly partition the effect of a covariate on treatment failure into its component parts. We illustrate the use and interpretation of additive models for the hazard rate or for the cumulative incidence function using data from a study of the efficacy of two preparative regimes for hematopoietic stem cell transplantation.
在癌症研究分析中，竞争风险普遍存在。最常见的竞争风险是缓解期复发和死亡。这两种风险是患者治疗失败的主要原因。在大多数医学论文中，协变量对三种结局（复发、缓解期死亡和治疗失败）的影响是通过不同的比例风险回归模型进行建模的。由于复发风险和缓解期死亡风险之和必定等于治疗失败风险，我们认为这种模型会导致内部不一致。我们认为，对于风险率或累积发病率函数而言，加法模型更为自然，并且这些模型能够将协变量对治疗失败的影响合理地分解为各个组成部分。我们使用一项关于两种造血干细胞移植预处理方案疗效研究的数据，说明了风险率或累积发病率函数加法模型的使用和解释。
