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Title: Multiparameter evidence synthesis in economic evaluation
Author: Leal, José Jorge Cabral Pinto
ISNI:       0000 0004 2725 5185
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2010
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This thesis explores the methodological and practical issues involved with synthesising the evidence required by economic decision models. Decision models represent a useful means of comparing alternative healthcare interventions in terms of their relative costs and effects. Cost-effectiveness estimates derived from these models along with the uncertainty around these estimates provide valuable information to guide decision makers when considering the implementation of interventions. Hence, care is required to ensure that these decisions are accurately represented by including all the relevant evidence in the model. This thesis demonstrates that decision modelling guidelines provide limited advice on how to synthesise evidence for the non-effectiveness parameters of a decision model. Furthermore, it is common to find a number of sources of evidence to inform these model parameters. Conversely, evidence may be unavailable or if it exists it may inform functions of these parameters rather than the individual model parameters. Hence, guidance is required on the best approaches to take account of these situations. Bayesian multi-parameter evidence synthesis (MPES) has recently been proposed as a method that can be used to synthesise evidence and address these issues. The thesis reviews the MPES, model fitting and evidence consistency methodologies. Two case studies are used to assess the value and generalisability of using MPES for decision analytic models; the focus is on the elicitation of expert opinion, meta-regression models and complex synthesis models. The advantages of MPES over traditional approaches for informing decision models are identified and discussed. The use of MPES methods for the purpose of decision modelling results in a valid and credible model, based on all available evidence, formally synthesised, systematically calibrated and checked for consistency and model fit. The thesis illustrates that these methods can be applied across very different disease areas. However, a caveat to their use is the time, resources and cross-discipline expertise required to build a MPES model as part of the economic evaluation. Interdisciplinary teams are a requirement to ensure the general adoption of these methods in health economics. Finally, it is recommended that MPES should be considered as one of the stages when developing a decision model.
Supervisor: Wolstenholme, Jane Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available