Abstract:
In this thesis the use of data scientific approaches in the life sciences is illustrated by means of contemporary prostate cancer risk models. Validation techniques are introduced and analytical confidence intervals for selected methods derived. In addition, diverse regression approaches to incorporate heterogeneous cohorts, an update of an online available risk calculator and machine learning methods are analyzed and compared.