Persistent URL of this record https://hdl.handle.net/1887/73914
Documents
-
- Download
- Title Pages_Contents
- open access
-
- Download
- Introduction
- open access
-
- Download
- Chapter 1
- open access
- Full text at publishers site
-
- Download
- Chapter 2
- open access
- Full text at publishers site
-
- Download
- Chapter 3
- open access
- Full text at publishers site
-
- Download
- Chapter 4
- open access
- Full text at publishers site
-
- Download
- Chapter 5
- open access
- Full text at publishers site
-
- Download
- Bibliography
- open access
-
- Download
- Summary in Dutch
- open access
-
- Download
- Propositions
- open access
In Collections
This item can be found in the following collections:
Dynamic prediction in event history analysis
Event history data are routinely collected either as a part of a study or in health registries and they can be used to create statistical models. The models can be used to make personalised predictions that accounts for a patient's specific characteristics. Dynamic prediction models are designed to make predictions not only from baseline, but also during the follow-up of the patient. Hence, predictions are updated as time progresses and incorporate the information that becomes available during follow-up.
In recent years, a number of new statistical methods for creating models for event history data have emerged, such as inverse probability...Show moreIn many healthcare settings it is of great interest to be able to predict the risk of events occurring in the future. Usually the interest is in predicting the probability that a patient will survive. In this case the event is the death of the patient, but the event could also be the diagnosis of a disease or hospital discharge.
Event history data are routinely collected either as a part of a study or in health registries and they can be used to create statistical models. The models can be used to make personalised predictions that accounts for a patient's specific characteristics. Dynamic prediction models are designed to make predictions not only from baseline, but also during the follow-up of the patient. Hence, predictions are updated as time progresses and incorporate the information that becomes available during follow-up.
In recent years, a number of new statistical methods for creating models for event history data have emerged, such as inverse probability weights and pseudo-observations. The objective of this thesis has been to contribute to the statistical methodology by extending the available methods to make dynamic predictions. The thesis focuses on two approaches for making dynamic predictions known as landmarking and joint-modelling.Show less
- All authors
- Grand, M.K.
- Supervisor
- Putter, H.
- Co-supervisor
- Vermeer, K.A.
- Committee
- Andersen, P.K.; Perme, M.P.; Goeman, J.
- Qualification
- Doctor (dr.)
- Awarding Institution
- Faculty of Medicine, Leiden University Medical Center (LUMC) , Leiden University
- Date
- 2019-06-13
- ISBN (print)
- 9789463803533
Funding
- Sponsorship
- This work was supported by funding from the European Community's Seventh Framework Programme FP7/2011: Marie Curie Initial Training Network MEDIASRES ("Novel Statistical Methodology for Diagnostic/Prognostic and Therapeutic Studies and Systematic Reviews"; www.mediasres-itn.eu) with the Grant Agreement Number 290025 and Netherlands Organisation for Health Research and Development TopZorg Grant 842005005.