Fitting and modeling cure in population-based cancer studies within the framework of flexible parametric survival models
Therese M.-L. Andersson
Department of Medical Epidemiology
and Biostatistics, Karolinska Institutet
Stockholm, Sweden
[email protected]
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Paul C. Lambert
Department of Health Sciences
University of Leicester
Leicester, UK
and
Department of Medical Epidemiology and Biostatistics
Karolinska Institutet
Stockholm, Sweden
[email protected]
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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 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.
View all articles by these authors:
Therese M.-L. Andersson, Paul C. Lambert
View all articles with these keywords:
stpm2, stpm2 postestimation, cure models, flexible parametric survival model, relative survival, survival analysis
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