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Analyzing Competing Risk Data Using the R timereg Package

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  • Scheike, Thomas H.
  • Zhang, Mei-Jie

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.

Suggested Citation

  • Scheike, Thomas H. & Zhang, Mei-Jie, 2011. "Analyzing Competing Risk Data Using the R timereg Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i02).
    • Handle: RePEc:jss:jstsof:v:038:i02
    DOI: http://hdl.handle.net/10.18637/jss.v038.i02
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    Cited by:

    1. Ayuso, Mercedes & Bermúdez, Lluís & Santolino, Miguel, 2015. "The dynamics of one-sided incomplete information in motor disputes," International Review of Law and Economics, Elsevier, vol. 41(C), pages 77-85.
    2. repec:jss:jstsof:47:i09 is not listed on IDEAS
    3. 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.
    4. Araújo, Artur & Meira-Machado, Luís & Roca-Pardiñas, Javier, 2014. "TPmsm: Estimation of the Transition Probabilities in 3-State Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i04).
    5. Helene C. W. Rytgaard & Frank Eriksson & Mark J. van der Laan, 2023. "Estimation of time‐specific intervention effects on continuously distributed time‐to‐event outcomes by targeted maximum likelihood estimation," Biometrics, The International Biometric Society, vol. 79(4), pages 3038-3049, December.
    6. Anthony Y. C. Kuk, 2020. "A Non‐Proportional Hazards Model with Hazard Ratio Functions Free from Covariate Values," International Statistical Review, International Statistical Institute, vol. 88(3), pages 715-727, December.
    7. repec:jss:jstsof:38:i01 is not listed on IDEAS
    8. Diego I. Gallardo & Mário de Castro & Héctor W. Gómez, 2021. "An Alternative Promotion Time Cure Model with Overdispersed Number of Competing Causes: An Application to Melanoma Data," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    9. Pao-sheng Shen, 2022. "Nonparametric estimation for competing risks survival data subject to left truncation and interval censoring," Computational Statistics, Springer, vol. 37(1), pages 29-42, March.
    10. Xavier Milhaud & Christophe Dutang, 2018. "Lapse tables for lapse risk management in insurance: a competing risk approach," Post-Print hal-01727669, HAL.
    11. Mogensen, Ulla B. & Ishwaran, Hemant & Gerds, Thomas A., 2012. "Evaluating Random Forests for Survival Analysis Using Prediction Error Curves," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i11).
    12. Gail E. Potter & Nicole Bohme Carnegie & Jonathan D. Sugimoto & Aldiouma Diallo & John C. Victor & Kathleen M. Neuzil & M. Elizabeth Halloran, 2022. "Using social contact data to improve the overall effect estimate of a cluster‐randomized influenza vaccination program in Senegal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 70-90, January.
    13. Thomas, Laine & Reyes, Eric M., 2014. "Tutorial: Survival Estimation for Cox Regression Models with Time-Varying Coe?cients Using SAS and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(c01).

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