Advanced statistical modelling in prostate cancer: From an individualized and public health perspective

Zhenwei Yang

Advanced statistical modelling in prostate cancer: From an individualized and public health perspective

Zhenwei Yang

Epidemiology

Research output: Types of Thesis › Doctoral Thesis › Internal

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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.

Original language	English
Awarding Institution	Erasmus University Rotterdam
Supervisors/Advisors	Rizopoulos, Dimitris, Supervisor Erler, Nicole, Co-supervisor Heijnsdijk, Eveline, Co-supervisor
Award date	8 Apr 2026
Place of Publication	Rotterdam
Print ISBNs	978-94-6537-264-8
Publication status	Published - 8 Apr 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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final dissertation Yang, Zhenwei Final published version, 41.9 MB

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title = "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.",

author = "Zhenwei Yang",

year = "2026",

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school = "Erasmus University Rotterdam",

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TY - BOOK

T1 - Advanced statistical modelling in prostate cancer

T2 - From an individualized and public health perspective

AU - Yang, Zhenwei

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N2 - 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.

AB - 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.

M3 - Doctoral Thesis

SN - 978-94-6537-264-8

CY - Rotterdam

ER -

Advanced statistical modelling in prostate cancer: From an individualized and public health perspective

Abstract

UN SDGs

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