ABSTRACT
There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime a
TABLE OF CONTENTS
part I|2 pages
Prognostic models for survival data using (clinical) infor- mation available at baseline, based on the Cox model
part II|2 pages
Prognostic models for survival data using (clinical) in- formation available at baseline, when the proportional haz- ards assumption of the Cox model is violated
part III|2 pages
Dynamic prognostic models for survival data using time-dependent information
part IV|2 pages
Dynamic prognostic models for survival data using ge- nomic data
