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Title: Summarising predictive ability of a survival model and applications in medical research
Author: Babak, C.-O.
Awarding Body: University of London
Current Institution: University College London (University of London)
Date of Award: 2008
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With the molecular revolution in medicine, many new potential prognostic and predictive factors are becoming available. However, whether new factors will lead to substantial improvement in the accuracy of prognostic assessments requires the use of a suitable per formance measure when considering different prognostic models. Several such measures have been proposed for use in survival analysis with a particular emphasis on measures proposed for the Cox proportional hazards model. However, there is no consensus of opinion on this issue. The proposed measures make use of a wide spectrum of techniques from information theory to statistical imputation. No comprehensive systematic summary of these measures has been done, and no adequate comparison of measures, theoretically or in practice, has been reported. This PhD studies the proposed measures systematically. It defines a set of criteria that a measure should possess in the context of survival analysis. Essential aspects of a measure are that it should be consistent under different degrees of censoring and sample size conditions it should also possess properties such as variable and parameter monotonicity. Desirable properties of a measure are robustness and extendability. This thesis compares the existing measures using these criteria discussing their strengths and shortcomings. From a practical point of view, a discussion of why these measures are important and what information they can provide in medical research, practical data analysis, and perhaps most importantly in prognostic modelling is presented. Data has been taken from completed randomised controlled trials in several diseases carried out by MRC Clinical Trials Unit and other research organisations. The measures that have the best properties will be applied to models fitted to these datasets. This allows us to quantify and assess the prognostic ability of the available prognostic factors in several diseases.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available