This article is a U.S. Government work and is in the public domain in the U.S.A.
Assessing cumulative incidence functions under the semiparametric additive risk model†
Article first published online: 7 JUL 2009
This article is a U.S. Government work and is in the public domain in the U.S.A. Published in 2009 by John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 28, Issue 22, pages 2748–2768, 30 September 2009
How to Cite
Hyun, S., Sun, Y. and Sundaram, R. (2009), Assessing cumulative incidence functions under the semiparametric additive risk model. Statist. Med., 28: 2748–2768. doi: 10.1002/sim.3640
- Issue published online: 4 SEP 2009
- Article first published online: 7 JUL 2009
- Manuscript Accepted: 20 MAY 2009
- Manuscript Received: 10 MAR 2008
- NSF. Grant Numbers: DMS-0304922, DMS-0604576
- NIH. Grant Number: 2 RO1 AI054165-04
- Intramural research program of Eunice Kennedy Shriver National Institute of Child Health and Human Development
- competing risks;
- survival analysis;
- cumulative incidence function;
- confidence interval;
- semiparametric model
In analyzing competing risks data, a quantity of considerable interest is the cumulative incidence function. Often, the effect of covariates on the cumulative incidence function is modeled via the proportional hazards model for the cause-specific hazard function. As the proportionality assumption may be too restrictive in practice, we consider an alternative more flexible semiparametric additive hazards model of (Biometrika 1994; 81:501–514) for the cause-specific hazard. This model specifies the effect of covariates on the cause-specific hazard to be additive as well as allows the effect of some covariates to be fixed and that of others to be time varying. We present an approach for constructing confidence intervals as well as confidence bands for the cause-specific cumulative incidence function of subjects with given values of the covariates. Furthermore, we also present an approach for constructing confidence intervals and confidence bands for comparing two cumulative incidence functions given values of the covariates. The finite sample property of the proposed estimators is investigated through simulations. We conclude our paper with an analysis of the well-known malignant melanoma data using our method. Published in 2009 by John Wiley & Sons, Ltd.