The main objective of this thesis was to develop clinically relevant survival models for patients with high-grade soft tissue sarcoma of the extremities, in particular the development and... Show moreThe main objective of this thesis was to develop clinically relevant survival models for patients with high-grade soft tissue sarcoma of the extremities, in particular the development and validation of prediction models for use in clinical practice. The interdisciplinary collaboration between the Mathematical Institute of Leiden University and the Leiden University Medical Center resulted in important contributions to the care of soft tissue sarcoma patients. Show less
In 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... 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
Rueten-Budde, A.J.; Praag, V.M. van; Sande, M.A.J. van de; Fiocco, M.; PERSARC Studygrp 2018
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.... Show moreM.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. Show less
Nicolaie, M.A.; Houwelingen, J.C. van; Witte, T.M. de; Putter, H. 2013
