Manca Jesenko, assistant
Faculty of Organizational Sciences, University of Maribor, Slovenia
Relative survival is
usually defined as the ratio of the observed survival function and the
expected survival function. Another approach, so called transformation
approach, defines relative survival for each individual as the value
which a subject reaches on the expected distribution function. Based on
these two approaches different methods for modeling relative survival
can be used. The standard approach is to use the additive model, only
rarely the multiplicative model is used. Both models assume certain
relationship between the observed and population hazard. With
transformation approach any model for analyzing standard survival data
can be used and the most obvious advantage of transformation approach is
the absence of any kind of assumptions concerning the relationship
between observed and population hazards. In this talk we will discuss
two methods to analyze transformed survival data. The first method is
the Buckley and James linear regression model which in contrast to other
methods in survival analysis focuses on modeling observed (transformed)
survival times, the second method is the Aalen linear regression model
which models the hazard function. Both models could offer some new
information and different insight into relative survival. We will
shortly present theoretical backgrounds of both models, the
possibilities to use them in relative survival and their application on
a real data set.

