  In generalized regression models the effect of continuous covariates is
commonly assumed to be linear. This assumption, however, may be too restrictive
in applications and may lead to biased effect estimates and decreased
predictive ability. While a multitude of alternatives for the flexible modeling
of continuous covariates have been proposed, methods that provide guidance for
choosing a suitable functional form are still limited. To address this issue,
we propose a detection algorithm that evaluates several approaches for modeling
continuous covariates and guides practitioners to choose the most appropriate
alternative. The algorithm utilizes a unified framework for tree-structured
modeling which makes the results easily interpretable. We assessed the
performance of the algorithm by conducting a simulation study. To illustrate
the proposed algorithm, we analyzed data of patients suffering from chronic
kidney disease.
