Regression analysis of competing risks data via semi-parametric additive hazard model
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- 作者：Xu Zhang (1) matxxz@langate.gsu.edu
Haci Akcin (1)
Hyun J. Lim (2) - 关键词：Competing risks – ; Additive hazard model – ; Cox model – ; Cumulative incidence function
- 刊名：Statistical Methods & Applications
- 出版时间：August 2011
- 出版年：2011
- 期刊代码：pc623_1618-2510
- 类别：mat
- 卷：20
- 期：3
- 页码：357-381
- 数据来源：sp
摘要
When the subjects in a study possess different demographic and disease characteristics and are exposed to more than one types of failure, a practical problem is to assess the covariate effects on each type of failure as well as on all-cause failure. The most widely used method is to employ the Cox models on each cause-specific hazard and the all-cause hazard. It has been pointed out that this method causes the problem of internal inconsistency. To solve such a problem, the additive hazard models have been advocated. In this paper, we model each cause-specific hazard with the additive hazard model that includes both constant and time-varying covariate effects. We illustrate that the covariate effect on all-cause failure can be estimated by the sum of the effects on all competing risks. Using data from a longitudinal study on breast cancer patients, we show that the proposed method gives simple interpretation of the final results, when the primary covariate effect is constant in the additive manner on each cause-specific hazard. Based on the given additive models on the cause-specific hazards, we derive the inferences for the adjusted survival and cumulative incidence functions.