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Title: Efficient Computation of Value of Information
Author: Brennan, Alan
ISNI:       0000 0001 2435 6499
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2007
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This thesis is concerned with computation of expected value of information (EVI). The topic is important because EVI methodology is a rational, coherent framework for prioritising and planning the design of biomedical and clinical research studies that represent an enormous expenditure world-wide. At the start of my research few studies existed. During the course of the PhD, my own work and that of other colleagues has been published and the uptake of the developing methods is increasing. The thesis contains a review of early literature as well as of the the emerging studies over the 5 years since my first work was done in 2002 (Chapter 2). Methods to compute partial expected value of perfect information are developed and tested in illustrative cost-utility decision models with non-linear net benefit functions and correlated parameters. Evaluation using nested Monte Carlo simulations is investigated and the number of inner and outer simulations required is explored (Chapter 3). The computation of expected value of sample information using nested Monte Carlo simulations combined with Bayesian updating of model parameters with conjugate distributions given simulated data is examined (Chapter 4). In Chapter 5, the development of a novel Bayesian approximation for posterior expectations is given and this is applied and tested in the computation of EVSI for an illustrative model again with normally distributed parameters. The application is further extended to a non-conjugate proportional hazards Weibull distribution, a common circumstance for clinical trial concerned with survival or time to event data (Chapter 6). The application of the Bayesian approximation in the Weibull model is then tested against 4 other methods for estimating the Bayesian up- dated Weibull parameters including the computationally intensive Markov Chain Monte Carlo (MCMC) approach which could be considered the gold standard (Chapter 7). The result of the methodological developments in this thesis and the testing on case studies is that some new approaches to computing EVI are now a;vailable. In many models this will improve the efficiency of computation, making possible EVI calculations in some previously infeasible circumstances. In Chapter 8, I summarise the achievements made in this work, how they relate to that of other scholars and the research agenda which still faces us. I conclude with the firm hope that EVI methods will begin to provide decision makers with clearer support when deciding on investments in further research.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available