Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.784850
Title: Risk prediction modelling in head and neck cancer : development and validation of a model using the UK Biobank
Author: McCarthy, C.
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2018
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Abstract:
Introduction and Aims: Head and Neck Cancer (HNC) is the sixth most common cancer worldwide and it causes significant morbidity and mortality. A risk prediction model could help to stratify patients according to risk of disease and be used in the design of clinical trials to aid selection of participants. This thesis concerns the development and validation of a risk prediction model for absolute risk of HNC, using the UK Biobank dataset. The changes in incidence of HNC in England between 2002-2011 will be explored and novel female-specific risk factors will be reviewed. Methods: The model has been developed within the UK Biobank dataset, using logistic regression. The internal validity of the model was assessed using discrimination and calibration statistics. The model was externally validated within a cohort of the UK Biobank not used to develop the original model. Results: The risk model developed contains variables for age, smoking, gender, alcohol, diet, household income, BMI, number of sexual partners, fruit consumption and exercise. The c-statistic was 0.67 and the model displayed good calibration. On external validation, the c-statistic was 0.64 with good calibration. Conclusions: Methods for assessing the implementation and impact of the model are discussed. The model has shown reasonable performance through internal and external validation methods. Risk prediction models have the potential to inform the design of future clinical trials in HNC and this could be translated to work in OED.
Supervisor: Field, John K. ; Bonnett, Laura J. ; Marcus, Michael ; Kirkham, Jamie Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.784850  DOI:
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