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Analyzing Competing Risk Data Using the R timereg Package

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  • Thomas H. Scheike
  • Mei-Jie Zhang
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    Abstract

    In this paper we describe flexible competing risks regression models using the comp.risk() function available in the timereg package for R based on Scheike et al. (2008). Regression models are specified for the transition probabilities, that is the cumulative incidence in the competing risks setting. The model contains the Fine and Gray (1999) model as a special case. This can be used to do goodness-of-fit test for the subdistribution hazards’ proportionality assumption (Scheike and Zhang 2008). The program can also construct confidence bands for predicted cumulative incidence curves. We apply the methods to data on follicular cell lymphoma from Pintilie (2007), where the competing risks are disease relapse and death without relapse. There is important non-proportionality present in the data, and it is demonstrated how one can analyze these data using the flexible regression models.

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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 38 ()
    Issue (Month): i02 ()
    Pages:

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    Handle: RePEc:jss:jstsof:38:i02

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    Web page: http://www.jstatsoft.org/

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    Cited by:
    1. Kathryn Grace & Stuart Sweeney, 2014. "Pathways to marriage and cohabitation in Central America," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(6), pages 187-226, January.

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