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A survival regression with cure fraction applied to cervical cancer

Author

Listed:
  • Vicente G. Cancho

    (ICMC/USP)

  • Elizbeth C. Bedia

    (ICMC/USP)

  • Gauss M. Cordeiro

    (UFPE)

  • Fábio Prataviera

    (ESALQ/USP)

  • Edwin M. M. Ortega

    (ESALQ/USP)

  • Ana P. J. E. Santo

    (ICMC/USP)

Abstract

A new survival model is proposed in the presence of surviving fractions and unobserved dispersion. It is obtained by considering several latent factors (or risks) that generated the observed lifetime which follows a generalized Poisson distribution, and it includes as a special case, the promotion time cure model. We explore maximum likelihood tools for inference issues by aid of the expectation maximization algorithm for estimating the parameters while model discrimination problem is treated by the aid of the likelihood ratio test. The new regression is applied to cervical cancer data to evaluate covariates effects in the cured fraction and non-cured group.

Suggested Citation

  • Vicente G. Cancho & Elizbeth C. Bedia & Gauss M. Cordeiro & Fábio Prataviera & Edwin M. M. Ortega & Ana P. J. E. Santo, 2023. "A survival regression with cure fraction applied to cervical cancer," Computational Statistics, Springer, vol. 38(1), pages 403-418, March.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:1:d:10.1007_s00180-022-01233-4
    DOI: 10.1007/s00180-022-01233-4
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    References listed on IDEAS

    as
    1. Thiago G. Ramires & Niel Hens & Gauss M. Cordeiro & Edwin M. M. Ortega, 2018. "Estimating nonlinear effects in the presence of cure fraction using a semi-parametric regression model," Computational Statistics, Springer, vol. 33(2), pages 709-730, June.
    2. Vicente Cancho & Josemar Rodrigues & Mario de Castro, 2011. "A flexible model for survival data with a cure rate: a Bayesian approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(1), pages 57-70.
    3. Ambagaspitiya, R.S. & Balakrishnan, N., 1994. "On the Compound Generalized Poisson Distributions," ASTIN Bulletin, Cambridge University Press, vol. 24(2), pages 255-263, November.
    4. Vicente G. Cancho & Márcia A. C. Macera & Adriano K. Suzuki & Francisco Louzada & Katherine E. C. Zavaleta, 2020. "A new long-term survival model with dispersion induced by discrete frailty," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(2), pages 221-244, April.
    5. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
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