Advanced statistical modelling in prostate cancer:From an individualized and public health perspective
Abstract
This thesis focuses on two methodologies for guiding prostate cancer health policies in the areas of screening and active surveillance (AS): (1) Joint models for longitudinal and time-to-event outcomes, which support personalized AS protocols at the individual level; and (2) Microsimulation models, which integrate real-world data to provide population-level evidence for protocol evaluation.
Fields of Study & Subjects
Prostate Cancer Diagnosis and Treatment
Statistical Methods and Bayesian Inference
Global Cancer Incidence and Screening
Publication details
- Journal / source
- EUR Research Repository (Erasmus University Rotterdam)
- Publication date
- 8 April 2026
- Language
- EN
- Document type
- Thesis