IDEAS home Printed from https://ideas.repec.org/p/dem/wpaper/wp-2003-010.html
   My bibliography  Save this paper

Unobserved heterogeneity in a model with cure fraction applied to breast cancer

Author

Listed:
  • Andreas Wienke

    (Max Planck Institute for Demographic Research, Rostock, Germany)

  • Paul Lichtenstein
  • Anatoli I. Yashin

    (Max Planck Institute for Demographic Research, Rostock, Germany)

Abstract

We suggest a cure-mixture model to analyze bivariate time-to-event data, as motivated by the paper of Chatterjee and Shih (2001, Biometrics 57, 779 - 786), but with a simpler estimation procedure and the correlated gamma-frailty model instead of the shared gamma-frailty model. This approach allows us to deal with left truncated and right censored lifetime data and accounts for heterogeneity as well as for an insusceptible (cure) fraction in the study population. We perform a simulation study to evaluate the properties of the estimates in the proposed model and apply it to breast cancer incidence data for 5,857 Swedish female monozygotic and dizygotic twin pairs from the so-called old cohort of the Swedish Twin Registry. This model is used to estimate the size of the susceptible fraction and the correlation between the frailties of the twin partners. Possible extensions, advantages and limitations of the proposed method are discussed.

Suggested Citation

  • Andreas Wienke & Paul Lichtenstein & Anatoli I. Yashin, 2003. "Unobserved heterogeneity in a model with cure fraction applied to breast cancer," MPIDR Working Papers WP-2003-010, Max Planck Institute for Demographic Research, Rostock, Germany.
    • Handle: RePEc:dem:wpaper:wp-2003-010
    DOI: 10.4054/MPIDR-WP-2003-010
    as

    Download full text from publisher

    File URL: https://www.demogr.mpg.de/papers/working/wp-2003-010.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.4054/MPIDR-WP-2003-010?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
    ---><---

    References listed on IDEAS

    as
    1. Ira M. Longini & M. Elizabeth Halloran, 1996. "A Frailty Mixture Model for Estimating Vaccine Efficacy," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(2), pages 165-173, June.
    2. Nilanjan Chatterjee & Joanna Shih, 2001. "A Bivariate Cure-Mixture Approach for Modeling Familial Association in Diseases," Biometrics, The International Biometric Society, vol. 57(3), pages 779-786, September.
    3. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
    4. Chris Elbers & Geert Ridder, 1982. "True and Spurious Duration Dependence: The Identifiability of the Proportional Hazard Model," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 49(3), pages 403-409.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Govert Ewout Bijwaard, 2014. "Multistate event history analysis with frailty," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(58), pages 1591-1620.
    2. Jaap H. Abbring & Gerard J. Van Den Berg, 2007. "The unobserved heterogeneity distribution in duration analysis," Biometrika, Biometrika Trust, vol. 94(1), pages 87-99.
    3. Bijwaard, Govert, 2011. "Unobserved Heterogeneity in Multiple-Spell Multiple-States Duration Models," IZA Discussion Papers 5748, Institute of Labor Economics (IZA).
    4. Abbring, Jaap H. & Berg, Gerard J. van den, 2000. "The non-parametric identification of the mixed proportional hazards competing risks model," Serie Research Memoranda 0024, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    5. Amy Ming-Fang Yen & Tony Hsiu-Hsi Chen, 2007. "Mixture Multi-state Markov Regression Model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(1), pages 11-21.
    6. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460, Elsevier.
    7. Yashin, Anatoli I. & Arbeev, Konstantin G. & Akushevich, Igor & Kulminski, Alexander & Akushevich, Lucy & Ukraintseva, Svetlana V., 2008. "Model of hidden heterogeneity in longitudinal data," Theoretical Population Biology, Elsevier, vol. 73(1), pages 1-10.
    8. Mette Gerster & Niels Keiding & Lisbeth B. Knudsen & Katrine Strandberg-Larsen, 2007. "Education and second birth rates in Denmark 1981-1994," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 17(8), pages 181-210.
    9. Su, Shu & Sherris, Michael, 2012. "Heterogeneity of Australian population mortality and implications for a viable life annuity market," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 322-332.
    10. Anna Damm, 2009. "Determinants of recent immigrants’ location choices: quasi-experimental evidence," Journal of Population Economics, Springer;European Society for Population Economics, vol. 22(1), pages 145-174, January.
    11. Haghani, Shermineh, 2014. "Modeling hedge fund lifetimes: A dependent competing risks framework with latent exit types," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 291-320.
    12. Adriaan Kalwij, 2014. "An empirical analysis of the importance of controlling for unobserved heterogeneity when estimating the income-mortality gradient," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 31(30), pages 913-940.
    13. Steven M. Shugan, 2006. "Editorial: Errors in the Variables, Unobserved Heterogeneity, and Other Ways of Hiding Statistical Error," Marketing Science, INFORMS, vol. 25(3), pages 203-216, 05-06.
    14. Wienke, Andreas & Kuss, Oliver, 2009. "Random effects Weibull regression model for occupational lifetime," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1249-1250, August.
    15. Alex Mota & Eder A. Milani & Vinicius F. Calsavara & Vera L. D. Tomazella & Jeremias Leão & Pedro L. Ramos & Paulo H. Ferreira & Francisco Louzada, 2021. "Weighted Lindley frailty model: estimation and application to lung cancer data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 561-587, October.
    16. Brito, Éder S. & Tomazella, Vera L.D. & Ferreira, Paulo H., 2022. "Statistical modeling and reliability analysis of multiple repairable systems with dependent failure times under perfect repair," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    17. Missov, Trifon I. & Finkelstein, Maxim, 2011. "Admissible mixing distributions for a general class of mixture survival models with known asymptotics," Theoretical Population Biology, Elsevier, vol. 80(1), pages 64-70.
    18. Shu Su & Michael Sherris, 2011. "Heterogeneity of Australian Population Mortality and Implications for a Viable Life Annuity Market," Working Papers 201103, ARC Centre of Excellence in Population Ageing Research (CEPAR), Australian School of Business, University of New South Wales.
    19. James W. Vaupel & Trifon Missov, 2014. "Unobserved population heterogeneity," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 31(22), pages 659-686.
    20. Bagdonavicius, Vilijandas & Nikulin, Mikhail, 2000. "On goodness-of-fit for the linear transformation and frailty models," Statistics & Probability Letters, Elsevier, vol. 47(2), pages 177-188, April.

    More about this item

    Keywords

    Sweden; breast; cancer; correlation; survival; twins;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

    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:dem:wpaper:wp-2003-010. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Peter Wilhelm (email available below). General contact details of provider: https://www.demogr.mpg.de/ .

    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.