Long-term modelling to inform cost-effectiveness analyses in diabetes
Diabetes develops over a long time, has substantial costs and is associated with high morbidity and mortality. Randomised trials in diabetes provide key effectiveness data but are limited in duration to inform long-term health gains and costs of interventions and guide healthcare reimbursement decisions. Computer simulation models of diabetes aim to support such evaluations . However, the model’s ability to reliably simulate disease progression is not guaranteed and the model/s might not be suited to evaluating effects of target intervention/s. Hence, new methods are often required. The project’s aim is to develop the methods for the assessment of long-term effects of aspirin in diabetes. The work will include reviews of current methods and data, evidence synthesis and model development and validation. The rich individual participant data of the 15,000-patient ASCEND (A Study of Cardiovascular Events iN Diabetes) trial (7.5 years mean follow up)  and other data will be used.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The project will start with an overview of existing long-term models in diabetes followed by:
• Validation of the performance of UKDPS outcomes model , the recommended diabetes model by NICE (UK), using the ASCEND data with a focus on performance in predicting cause-specific mortality and morbidity overall and in different categories of participants in ASCEND.
• Development of a substantive adaptation to the UKPDS outcomes model to allow long-term effects of aspirin on cardiovascular outcomes, gastrointestinal bleeds, and (colorectal) cancer to be evaluated or a novel diabetes model using UKPDS model, ASCEND data and other published data.
• Development of quality of life and healthcare costs’ evaluative frameworks that include cardiovascular outcomes, serious bleeding events and cancers, endpoints of particular interest in the assessment of aspirin’s effects.
• Value of reducing decision uncertainty will be evaluated with respect to particular parameters, such as aspirin’s effects on cancer.
Value Health, 2013. 16: p. 670; http://rum.ctsu.ox.ac.uk/~ascend/; Diabetologia, 2013. 56(9): p. 1925-33.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
Training will be offered in the fields of Health Economics; Decision Analytic modelling; Advanced Survival Analysis; Systematic review and Meta-analysis and Value-of-Information analyses as relevant. Collaboration with external experts is encouraged.
First- or strong upper second-class undergraduate degree (or equivalent international qualifications) is required; master degree and/or relevant experience is highly desirable. A degree in a quantitative discipline (eg, Statistics, Mathematics, Economics, Epidemiology, Operation Research) or, if in different discipline, the inclusion of substantial and well-graded quantitative component, is required. Applicants whose first language is not English are required to provide evidence of proficiency in English at the higher level.