Competing risks predictions with different time scales under the additive risk model. (7th June 2022)

• Record Type: Journal Article
• Authors: Lee, Minjung; Fine, Jason P.
• Is Part Of: Statistics in medicine. Volume 41:Number 20(2022)
• Page Start: 3941
• Page End: 3957
• Publication Date: 2022-06-07
• Subjects: additive risk model -- cause‐specific hazard function -- competing risks -- cumulative incidence function -- multiple time scales; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.9485
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : In the analysis for competing risks data, regression modeling of the cause‐specific hazard functions has been usually conducted using the same time scale for all event types. However, when the true time scale is different for each event type, it would be appropriate to specify regression models for the cause‐specific hazards on different time scales for different event types. Often, the proportional hazards model has been used for regression modeling of the cause‐specific hazard functions. However, the proportionality assumption may not be appropriate in practice. In this article, we consider the additive risk model as an alternative to the proportional hazards model. We propose predictions of the cumulative incidence functions under the cause‐specific additive risk models employing different time scales for different event types. We establish the consistency and asymptotic normality of the predicted cumulative incidence functions under the cause‐specific additive risk models specified on different time scales using empirical processes and derive consistent variance estimators of the predicted cumulative incidence functions. Through simulation studies, we show that the proposed prediction methods perform well. We illustrate the methods using stage III breast cancer data obtained from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute.