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Fitting and modeling cure in population-based cancer studies within the framework of flexible parametric survival models

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  • Therese M.-L. Andersson

    (Karolinska Institutet)

  • Paul C. Lambert

    (University of Leicester
    Karolinska Institutet)

Abstract

When the mortality among a cancer patient group returns to the same level as in the general population, that is, when the patients no longer experi- ence excess mortality, the patients still alive are considered “statistically cured”. Cure models can be used to estimate the cure proportion as well as the survival function of the “uncured”. One limitation of parametric cure models is that the functional form of the survival of the uncured has to be specified. It can some- times be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagno- sis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models. Here we use flexible parametric survival models that incorporate cure as a special case to estimate the cure proportion and the survival of the uncured. Flexible parametric survival models use splines to model the underlying hazard function; therefore, no parametric distribution has to be specified. We have updated the stpm2 command for flexible parametric models to enable cure modeling.

Suggested Citation

  • Therese M.-L. Andersson & Paul C. Lambert, 2012. "Fitting and modeling cure in population-based cancer studies within the framework of flexible parametric survival models," Stata Journal, StataCorp LP, vol. 12(4), pages 623-638, December.
    • Handle: RePEc:tsj:stataj:v:12:y:2012:i:4:p:623-638
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    References listed on IDEAS

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    1. P. C. Lambert & P. W. Dickman & C. L. Weston & J. R. Thompson, 2010. "Estimating the cure fraction in population‐based cancer studies by using finite mixture models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 35-55, January.
    2. Paul C. Lambert, 2007. "Modeling of the cure fraction in survival studies," Stata Journal, StataCorp LP, vol. 7(3), pages 351-375, September.
    3. Patrick Royston, 2001. "Flexible alternatives to the Cox model, and more," Stata Journal, StataCorp LP, vol. 1(1), pages 1-28, November.
    4. Paul C. Lambert & Patrick Royston, 2009. "Further development of flexible parametric models for survival analysis," Stata Journal, StataCorp LP, vol. 9(2), pages 265-290, June.
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