Dynamic aspects of competing risks with application to medical data

Leiden Repository

Dynamic aspects of competing risks with application to medical data

Title: Dynamic aspects of competing risks with application to medical data
Author: Nicolaie, Mioara Alina
Publisher: Department of Medical Statistics and Bioinformatics, Faculty of Medicine, Leiden University Medical Center (LUMC), Leiden University
Issue Date: 2014-01-08
Keywords: Survival analysis
Competing risks
Cause-specific hazards
Relative hazards
Dynamic prediction
Dynamic pseudo-observation
Quasi-likelihood
Working correlation
Cumulative incidence
Survival function
Abstract: M.A. Nicolaie focuses in this thesis on inference in survival models for survival data with competing risks. The author introduces a new approach to competing risks data, called vertical modeling. It is built on natural observable quantities in competing risks, that is, it quantifies 1. the chance that a failure occurs, irrespective of its cause and 2. conditionally that a failure occurred, it quantifies the risk that the event of failure is ascertained to a certain type of failure. Another appealing feature of vertical modeling which is discussed is that it deals with competing risks when missing causes of failure occur. Next, the author tackles the topic of dynamic prediction in competing risks, a topical subject nowadays. She uses two different approaches, one which is based on modeling the cause-specific hazards and one which is based on modeling the dynamic pseudo-observations associated to the cumulative incidence functions. The results presented in this thesis provide key messages on the use of competing risks methods in different fields such as epidemiology, medicine, demography.M.A. Nicolaie focuses in this thesis on inference in survival models for survival data with competing risks. The author introduces a new approach to competing risks data, called vertical modeling. It is built on natural observable quantities in competing risks, that is, it quantifies 1. the chance that a failure occurs, irrespective of its cause and 2. conditionally that a failure occurred, it quantifies the risk that the event of failure is ascertained to a certain type of failure. Another appealing feature of vertical modeling which is discussed is that it deals with competing risks when missing causes of failure occur. Next, the author tackles the topic of dynamic prediction in competing risks, a topical subject nowadays. She uses two different approaches, one which is based on modeling the cause-specific hazards and one which is based on modeling the dynamic pseudo-observations associated to the cumulative incidence functions. The results presented in this thesis provide key messages on the use of competing risks methods in different fields such as epidemiology, medicine, demography.
Description: Promotores: H. Putter, H.C. van Houwelingen
With Summary in Dutch
Faculty: LUMC
Citation: Nicolaie, M.A., 2014, Doctoral Thesis, Leiden University
ISBN: 9789461823847
Sponsor: Research leading to this thesis was supported by the Netherlands Organization for Scienti c Research Grant ZONMW-912-07-018'Prognostic modeling and dynamic prediction for competing risks and multi-state models'.
Handle: http://hdl.handle.net/1887/22988
 

Files in this item

Description Size View
application/pdf Full Text 1.858Mb View/Open
application/pdf Cover 2.774Mb View/Open
application/pdf Title Pages_Contents 172.8Kb View/Open
application/pdf Chapter 1 324.5Kb View/Open
application/pdf Chapter 2 954.1Kb View/Open Full text at publisher site
application/pdf Chapter 3 439.6Kb View/Open Full text at publisher site
application/pdf Chapter 4 553.7Kb View/Open Full text at publisher site
application/pdf Chapter 5 596.3Kb View/Open Full text at publisher site
application/pdf Chapter 6 466.7Kb View/Open
application/pdf Bibliography 174.8Kb View/Open
application/pdf Summary in Dutch 109.4Kb View/Open
application/pdf Publications_Curriculum Vitae_Acknowledgements 133.3Kb View/Open
application/pdf Propositions 90.20Kb View/Open

This item appears in the following Collection(s)