A. Bauer, G. Zauner, C. Urach:
"Evaluation of Risk Factors for Parametrization of Cancer Models";
Simulation Notes Europe, 25 (2015), 3-4; S. 141 - 144.

Kurzfassung englisch:
Abstract. Cancer is the second most cause of death in
Austria and around 38000 people are diagnosed with
cancer each year [1]. The goal of this paper is to analyze
methods for evaluation of risk factors in order to parametrize
a micro simulation model for cancer prevalence.
The focus of this paper is on modeling the survival time.
This is done by the methods of survival analysis and
model selection. Firstly, the survival function is estimated
by the Kaplan-Meier estimate. Afterwards, a Cox proportional
hazards regression is performed with all possible
sets of parameters. These models are tested by twos
with the likelihood ratio test in order to compare them.
Another approach is the so-called Lasso method. This
method puts a constraint on the sum of the absolute
values of the regression coefficients and in most cases
forces some of the coefficients to go to zero. The Akaike
Information Criterion is also applied. All three methods
are compared and the parameters which are supported,
at least to a certain extent, by all of them are included in
the estimation of the survival time of the prevalence

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