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

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 experience 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 sometimes 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 diagnosis, 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.


Issue Date:
2012
Publication Type:
Journal Article
DOI and Other Identifiers:
st0165_1 (Other)
PURL Identifier:
http://purl.umn.edu/231772
Published in:
Stata Journal, Volume 12, Number 4
Page range:
623-638
Total Pages:
18

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 Record created 2017-04-01, last modified 2017-08-29

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