Updating en evaluatie van polytome logistische regressie modellen.

Risk prediction models for diagnostic or prognostic outcomes are usefultools for clinical decision support. Most commonly, a dichotomous outcome (e.g. a benign or malignant tumor) is considered. Especially in diagnostic problems, however, a differential diagnosis often includes more levels than categorization of subjects as diseased versus non-diseased(e.g. a benign, borderline or invasive tumor). </>
</>
Methods for updating existing risk prediction models, i.e. adjusting an existing model in order to improve predictions from future patients in a new and different setting, had already been suggested for dichotomous models but did not yet exist for multinomial models. Closely related, the aspect ofcalibration of multinomial risk prediction models, i.e. the reliabilityof the predicted risks, had not been studied extensively. Therefore, inthis dissertation we extended calibration statistics, calibration plotsas well as updating techniques to prediction models for polytomous outcomes based on multinomial logistic regression.  </></>

Keywords:Multi-class models, Evaluation of models, Updating of models

Disciplines:Applied mathematics in specific fields, Computer architecture and networks, Distributed computing, Information sciences, Information systems, Programming languages, Scientific computing, Theoretical computer science, Visual computing, Other information and computing sciences, Modelling, Biological system engineering, Signal processing, Control systems, robotics and automation, Design theories and methods, Mechatronics and robotics, Computer theory

Project type:PhD project