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Biostatistics Advance Access first published online on April 24, 2006
This version published online on February 19, 2007
Biostatistics, doi:10.1093/biostatistics/kxj040
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Parametric regression on cumulative incidence function

Jong-Hyeon Jeong*

Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA jeong{at}nsabp.pitt.edu

Jason P. Fine

Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53706, USA

* To whom correspondence should be addressed.

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.

Keywords: Breast cancer; Clinical trial; Competing risks; Cumulative incidence; Cure model; Improper distribution; Regression; Transformation model

Received January 12, 2006; revised April 19, 2006; accepted for publication April 21, 2006.


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