Direct parametric inference for the cumulative incidence function
Direct parametric inference for the cumulative incidence function
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DOI：
10.1111/j.1467-9876.2006.00532.x
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发表时间：
2006-01-01
影响因子：
1.6
通讯作者：
Fine, J
中科院分区：
文献类型：
--
作者：
Jeong, JH;Fine, J
在乳腺癌三期临床试验中收集的生存数据中，患者可能经历不止一次事件，包括原始癌症的复发、新的原发癌症和死亡。放射肿瘤学家通常对比较局部或区域复发的模式作为第一事件感兴趣，以确定手术后需要接受放射治疗的患者亚群。累积关联函数提供在存在其他竞争事件的情况下局部区域复发的累积概率的估计。提出了一种简单的Gompertz分布来直接对累积关联函数进行参数化。累积关联函数的模型解释比通常的特定原因的危险参数更自然。最大似然分析用于同时估计所有原因的累积关联函数的参数模型。将参数累积关联方法应用于国家外科辅助乳肠项目的数据集，并与基于参数原因特定风险模型和非参数累积关联估计的分析进行了比较。
In survival data that are collected from phase III clinical trials on breast cancer, a patient may experience more than one event, including recurrence of the original cancer, new primary cancer and death. Radiation oncologists are often interested in comparing patterns of local or regional recurrences alone as first events to identify a subgroup of patients who need to be treated by radiation therapy after surgery. The cumulative incidence function provides estimates of the cumulative probability of locoregional recurrences in the presence of other competing events. A simple version of the Gompertz distribution is proposed to parameterize the cumulative incidence function directly. The model interpretation for the cumulative incidence function is more natural than it is with the usual cause-specific hazard parameterization. Maximum likelihood analysis is used to estimate simultaneously parametric models for cumulative incidence functions of all causes. The parametric cumulative incidence approach is applied to a data set from the National Surgical Adjuvant Breast and Bowel Project and compared with analyses that are based on parametric cause-specific hazard models and nonparametric cumulative incidence estimation.