IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v37y2010i10p1625-1636.html
   My bibliography  Save this article

The generalized exponential cure rate model with covariates

Author

Listed:
  • Nandini Kannan
  • Debasis Kundu
  • P. Nair
  • R. C. Tripathi

Abstract

In this article, we consider a parametric survival model that is appropriate when the population of interest contains long-term survivors or immunes. The model referred to as the cure rate model was introduced by Boag 1 in terms of a mixture model that included a component representing the proportion of immunes and a distribution representing the life times of the susceptible population. We propose a cure rate model based on the generalized exponential distribution that incorporates the effects of risk factors or covariates on the probability of an individual being a long-time survivor. Maximum likelihood estimators of the model parameters are obtained using the the expectation-maximisation (EM) algorithm. A graphical method is also provided for assessing the goodness-of-fit of the model. We present an example to illustrate the fit of this model to data that examines the effects of different risk factors on relapse time for drug addicts.

Suggested Citation

  • Nandini Kannan & Debasis Kundu & P. Nair & R. C. Tripathi, 2010. "The generalized exponential cure rate model with covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(10), pages 1625-1636.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1625-1636
    DOI: 10.1080/02664760903117739
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903117739
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664760903117739?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vicente Cancho & Heleno Bolfarine, 2001. "Modeling the presence of immunes by using the exponentiated-Weibull model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(6), pages 659-671.
    2. Song, Peter X.K. & Fan, Yanqin & Kalbfleisch, John D., 2005. "Maximization by Parts in Likelihood Inference," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1145-1158, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Lemonte, Artur J., 2013. "A new exponential-type distribution with constant, decreasing, increasing, upside-down bathtub and bathtub-shaped failure rate function," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 149-170.
    2. Saralees Nadarajah, 2011. "The exponentiated exponential distribution: a survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(3), pages 219-251, September.
    3. Jorge Alberto Achcar & Em�lio Augusto Coelho-Barros & Josmar Mazucheli, 2013. "Block and Basu bivariate lifetime distribution in the presence of cure fraction," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 1864-1874, September.
    4. Suvra Pal & Souvik Roy, 2021. "On the estimation of destructive cure rate model: A new study with exponentially weighted Poisson competing risks," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(3), pages 324-342, August.
    5. Debasis Kundu & Rameshwar Gupta, 2011. "Absolute continuous bivariate generalized exponential distribution," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 169-185, June.
    6. S. Mirhosseini & M. Amini & D. Kundu & A. Dolati, 2015. "On a new absolutely continuous bivariate generalized exponential distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 61-83, March.
    7. Muhammad H Tahir & Gauss M. Cordeiro, 2016. "Compounding of distributions: a survey and new generalized classes," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-35, December.
    8. Miroslav Ristić & Debasis Kundu, 2015. "Marshall-Olkin generalized exponential distribution," METRON, Springer;Sapienza Università di Roma, vol. 73(3), pages 317-333, December.

    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. Manabu Asai & Michael McAleer, 2009. "Dynamic Conditional Correlations for Asymmetric Processes," CARF F-Series CARF-F-168, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    2. Peng Zhang & Peter X.-K. Song & Annie Qu & Tom Greene, 2008. "Efficient Estimation for Patient-Specific Rates of Disease Progression Using Nonnormal Linear Mixed Models," Biometrics, The International Biometric Society, vol. 64(1), pages 29-38, March.
    3. Dawei Zhang & Zhuo (June) Cheng & Hasan A. Qurban H. Mohammad & Barrie R. Nault, 2015. "Research Commentary—Information Technology Substitution Revisited," Information Systems Research, INFORMS, vol. 26(3), pages 480-495, September.
    4. Frazier, David T. & Renault, Eric, 2017. "Efficient two-step estimation via targeting," Journal of Econometrics, Elsevier, vol. 201(2), pages 212-227.
    5. Hautsch, Nikolaus & Okhrin, Ostap & Ristig, Alexander, 2014. "Efficient iterative maximum likelihood estimation of high-parameterized time series models," SFB 649 Discussion Papers 2014-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Amado, Cristina & Teräsvirta, Timo, 2013. "Modelling volatility by variance decomposition," Journal of Econometrics, Elsevier, vol. 175(2), pages 142-153.
    7. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    8. Ulf Schepsmeier & Jakob Stöber, 2014. "Derivatives and Fisher information of bivariate copulas," Statistical Papers, Springer, vol. 55(2), pages 525-542, May.
    9. Nikolaus Hautsch & Ostap Okhrin & Alexander Ristig, 2023. "Maximum-Likelihood Estimation Using the Zig-Zag Algorithm," Journal of Financial Econometrics, Oxford University Press, vol. 21(4), pages 1346-1375.
    10. Anthony D. Hall & Annastiina Silvennoinen & Timo Teräsvirta, 2023. "Building Multivariate Time-Varying Smooth Transition Correlation GARCH Models, with an Application to the Four Largest Australian Banks," Econometrics, MDPI, vol. 11(1), pages 1-37, February.
    11. Silvennoinen, Annastiina & Teräsvirta, Timo, 2024. "Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model," Econometrics and Statistics, Elsevier, vol. 32(C), pages 57-72.
    12. Long, Xiangdong & Su, Liangjun & Ullah, Aman, 2011. "Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 109-125.
    13. Edward W. Frees & Gee Lee & Lu Yang, 2016. "Multivariate Frequency-Severity Regression Models in Insurance," Risks, MDPI, vol. 4(1), pages 1-36, February.
    14. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    15. Li, Lihui & Wen, Tao, 2013. "Estimation of C-MGARCH models based on the MBP method," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 665-673.
    16. Okhrin, Ostap & Okhrin, Yarema & Schmid, Wolfgang, 2013. "On the structure and estimation of hierarchical Archimedean copulas," Journal of Econometrics, Elsevier, vol. 173(2), pages 189-204.
    17. Jiang, Bin & Yang, Yanrong & Gao, Jiti & Hsiao, Cheng, 2021. "Recursive estimation in large panel data models: Theory and practice," Journal of Econometrics, Elsevier, vol. 224(2), pages 439-465.
    18. Amado, Cristina & Teräsvirta, Timo, 2014. "Modelling changes in the unconditional variance of long stock return series," Journal of Empirical Finance, Elsevier, vol. 25(C), pages 15-35.
    19. Noureldin, Diaa & Shephard, Neil & Sheppard, Kevin, 2014. "Multivariate rotated ARCH models," Journal of Econometrics, Elsevier, vol. 179(1), pages 16-30.
    20. Hafner, Christian M. & Reznikova, Olga, 2010. "Efficient estimation of a semiparametric dynamic copula model," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2609-2627, November.

    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:taf:japsta:v:37:y:2010:i:10:p:1625-1636. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.