Parametric regression on cumulative incidence function
Parametric regression on cumulative incidence function
复制标题
DOI：
10.1093/biostatistics/kxj040
复制
发表时间：
2007-04-01
期刊：
影响因子：
2.1
通讯作者：
Fine, Jason P.
中科院分区：
文献类型：
--
作者：
Jeong, Jong-Hyeon;Fine, Jason P.
我们提出了具有竞争风险数据的累积关联函数的参数回归分析。对于感兴趣事件的不适当基线子分布，使用了一种简单的Gompertz分布形式。对于参数模型，包括灵活的广义比值率模型，开发了回归参数和相关累积关联函数的最大似然推断。在参数设置中，对发生原因特异性事件的患者的长期比例的估计是直接的。简单的拟合优度检验讨论了评估一个固定的赔率假设。参数回归方法与现有的乳腺癌数据集的半参数回归分析进行了比较，其中累积复发率是感兴趣的。结果表明，基于似然的累积关联函数参数分析是替代半参数分析的一种实用方法。
We propose parametric regression analysis of cumulative incidence function with competing risks data. A simple form of Gompertz distribution is used for the improper baseline subdistribution of the event of interest. Maximum likelihood inferences on regression parameters and associated cumulative incidence function are developed for parametric models, including a flexible generalized odds rate model. Estimation of the long-term proportion of patients with cause-specific events is straightforward in the parametric setting. Simple goodness-of-fit tests are discussed for evaluating a fixed odds rate assumption. The parametric regression methods are compared with an existing semiparametric regression analysis on a breast cancer data set where the cumulative incidence of recurrence is of interest. The results demonstrate that the likelihood-based parametric analyses for the cumulative incidence function are a practically useful alternative to the semiparametric analyses.