Zhang Mei-Jie, Zhang Xu, Scheike Thomas H
Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, U.S.A. Tel: +1 414-456-8375;
Expert Rev Clin Pharmacol. 2008 May 1;1(3):391-400. doi: 10.1586/17512433.1.3.391.
A frequent occurrence in medical research is that a patient is subject to different causes of failure, where each cause is known as a competing risk. The cumulative incidence curve is a proper summary curve, showing the cumulative failure rates over time due to a particular cause. A common question in medical research is to assess the covariate effects on a cumulative incidence function. The standard approach is to construct regression models for all cause-specific hazard rate functions and then model a covariate-adjusted cumulative incidence curve as a function of all cause-specific hazards for a given set of covariates. New methods have been proposed in recent years, emphasizing direct assessment of covariate effects on cumulative incidence function. Fine and Gray proposed modeling the effects of covariates on a subdistribution hazard function. A different approach is to directly model a covariate-adjusted cumulative incidence function, including a pseudovalue approach by Andersen and Klein and a direct binomial regression by Scheike, Zhang and Gerds. In this paper, we review the standard and new regression methods for modeling a cumulative incidence function, and give the sources of computer packages/programs that implement these regression models. A real bone marrow transplant data set is analyzed to illustrate various regression methods.
在医学研究中经常出现的情况是，患者会面临不同的失败原因，每种原因都被称为竞争风险。累积发病率曲线是一种合适的汇总曲线，它显示了特定原因导致的随时间累积的失败率。医学研究中的一个常见问题是评估协变量对累积发病率函数的影响。标准方法是为所有特定原因的风险率函数构建回归模型，然后将协变量调整后的累积发病率曲线建模为给定协变量集下所有特定原因风险的函数。近年来提出了新的方法，强调直接评估协变量对累积发病率函数的影响。Fine和Gray提出对协变量对亚分布风险函数的影响进行建模。另一种不同的方法是直接对协变量调整后的累积发病率函数进行建模，包括Andersen和Klein的伪值方法以及Scheike、Zhang和Gerds的直接二项回归。在本文中，我们回顾了用于对累积发病率函数进行建模的标准回归方法和新回归方法，并给出了实现这些回归模型的计算机软件包/程序的来源。分析了一个真实的骨髓移植数据集以说明各种回归方法。
