IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v34y1985i3p201-211.html
   My bibliography  Save this article

A Mixture Model for the Regression Analysis of Competing Risks Data

Author

Listed:
  • Martin G. Larson
  • Gregg E. Dinse

Abstract

A parametric mixture model provides a regression framework for analysing failure‐time data that are subject to censoring and multiple modes of failure. The regression context allows us to adjust for concomitant variables and to assess their effects on the joint distribution of time and type of failure. The mixing parameters correspond to the marginal probabilities of the various failure types and are modelled as logistic functions of the covariates. The hazard rate for each conditional distribution of time to failure, given type of failure, is modelled as the product of a piece‐wise exponential function of time and a log‐linear function of the covariates. An EM algorithm facilitates the maximum likelihood analysis and illuminates the contributions of the censored observations. The methods are illustrated with data from a heart transplant study and are compared with a cause‐specific hazard analysis. The proposed mixture model can also be used to analyse multivariate failure‐time data.

Suggested Citation

  • Martin G. Larson & Gregg E. Dinse, 1985. "A Mixture Model for the Regression Analysis of Competing Risks Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(3), pages 201-211, November.
    • Handle: RePEc:bla:jorssc:v:34:y:1985:i:3:p:201-211
    DOI: 10.2307/2347464
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2347464
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2347464?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rebecca A. Betensky & David A. Schoenfeld, 2001. "Nonparametric Estimation in a Cure Model with Random Cure Times," Biometrics, The International Biometric Society, vol. 57(1), pages 282-286, March.
    2. Mioara Alina Nicolaie & Jeremy M. G. Taylor & Catherine Legrand, 2019. "Vertical modeling: analysis of competing risks data with a cure fraction," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 1-25, January.
    3. Angelica Hernandez-Quintero & Jean-François Dupuy & Gabriel Escarela, 2011. "Analysis of a semiparametric mixture model for competing risks," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(2), pages 305-329, April.
    4. repec:jss:jstsof:02:i07 is not listed on IDEAS
    5. Contreras-Cristan, Alberto, 2007. "Using the EM algorithm for inference in a mixture of distributions with censored but partially identifiable data," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2769-2781, February.
    6. Pierpaolo De Blasi & Nils L. Hjort, 2007. "The Bernstein-Von Mises Theorem in Semiparametric Competing Risks Models," ICER Working Papers - Applied Mathematics Series 17-2007, ICER - International Centre for Economic Research.
    7. Choi, K. C. & Zhou, X., 2002. "Large Sample Properties of Mixture Models with Covariates for Competing Risks," Journal of Multivariate Analysis, Elsevier, vol. 82(2), pages 331-366, August.
    8. S. R. Haile & J.-H. Jeong & X. Chen & Y. Cheng, 2016. "A 3-parameter Gompertz distribution for survival data with competing risks, with an application to breast cancer data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2239-2253, September.
    9. Beenstock, Michael & Rahav, Giora, 2002. "Testing Gateway Theory: do cigarette prices affect illicit drug use?," Journal of Health Economics, Elsevier, vol. 21(4), pages 679-698, July.
    10. Beilin Jia & Donglin Zeng & Jason J. Z. Liao & Guanghan F. Liu & Xianming Tan & Guoqing Diao & Joseph G. Ibrahim, 2022. "Mixture survival trees for cancer risk classification," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 356-379, July.
    11. Xu Ruimin & McNicholas Paul D & Desmond Anthony F & Darlington Gerarda A, 2011. "A First Passage Time Model for Long-Term Survivors with Competing Risks," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-15, May.
    12. Okumu Argan Wekesa & Mwalili Samuel & Mwita Peter, 2012. "Modelling Credit Risk for Personal Loans Using Product-Limit Estimator," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 3(1), pages 22-32, January.
    13. Cheng Yu, 2009. "Modeling Cumulative Incidences of Dementia and Dementia-Free Death Using a Novel Three-Parameter Logistic Function," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-19, November.
    14. Yingwei Peng & Keith B. G. Dear, 2000. "A Nonparametric Mixture Model for Cure Rate Estimation," Biometrics, The International Biometric Society, vol. 56(1), pages 237-243, March.
    15. N. Balakrishnan & M. V. Koutras & F. S. Milienos & S. Pal, 2016. "Piecewise Linear Approximations for Cure Rate Models and Associated Inferential Issues," Methodology and Computing in Applied Probability, Springer, vol. 18(4), pages 937-966, December.
    16. Wycinka Ewa, 2019. "Competing Risk Models of Default in the Presence of Early Repayments," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(2), pages 99-120, June.
    17. Sankaran, P.G. & Anisha, P., 2012. "Additive hazards models for gap time data with multiple causes," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1454-1462.
    18. Mo Leo S. F. & Yau Kelvin K. W., 2010. "Survival Mixture Model for Credit Risk Analysis," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 4(2), pages 1-20, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssc:v:34:y:1985:i:3:p:201-211. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.