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Title: Modelling health-related quality of life data for economic evaluation of cancer treatments : applications in lung cancer
Author: Khan, Iftekhar
ISNI:       0000 0004 7229 7456
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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INTRODUCTION: The annual economic burden of treating cancer to the National Health Service (NHS) in the United Kingdom (UK) is over £15 billion; and for non small cell lung cancer (NSCLC), one of the leading causes of cancer deaths in the world, this is £2.4 billion. Economic evaluation plays an essential role in assessing the relative value of lung cancer treatments. Modelling (HRQoL) data is fundamental in determining the cost-effectiveness of cancer treatments. This thesis aims to investigate modelling of HRQoL data collected from lung cancer patients for economic evaluation. In particular, the role of modelling to improve utility prediction is investigated. The sensitivity of disease specific and generic HRQoL measures are also explored. In addition, methods to extrapolate utilities beyond cancer progression and identifying a selection procedure from relevant published algorithms are developed. METHODS: Data from two clinical trials and a prospective observational study in NSCLC patients were designed and executed to develop several mapping models (Linear, Non-Linear, Joint, and Bayesian). The sensitivity of EQ-5D-3L and EQ-5D-5L were compared with a cancer specific measure (QLQ-C30). Simulation methods were used to develop an approach for selecting algorithms. RESULTS: Two and three-part Beta-Binomial models improve predictions. Joint models also contribute to improved prediction of utilities. Bayesian Networks may help reduce the over-prediction in poor health states. The EQ-5D-5L offers better mapping and is more sensitive for detecting treatment benefit compared to EQ-5D-3L. It is also viable to develop decision criteria for selecting between several published algorithms. CONCLUSION: Methodological improvements in modelling HRQoL for the economic evaluation of cancer treatments have been demonstrated. Improvements in model structure, prediction and selection are empirically demonstrated.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available