EXPLORING THE USE OF VALUE OF INFORMATION METHODS TO PRIORITISE RESEARCH TO ADDRESS THE TREATMENT UNCERTAINTIES IDENTIFIED BY THE JAMES LIND ALLIANCE PRIORITY SETTING PARTNERSHIPS

Author(s)

Sach T, McManus E
University of East Anglia, Norwich, UK

Economic analysis is regularly used to inform decisions on allocating healthcare budgets but not routinely for allocating health research budgets which may mean that the research budget is not delivering value for money. The study aims to use 'value of information' analysis to prioritise research funding across an entire clinical area. In particular, exploring the usefulness of such methods in prioritising the treatment uncertainties identified by the James Lind Alliance (JLA) Priority Setting Partnership (PSP) for atopic eczema. Whilst the research will primarily identify what the future research priorities within eczema should be, it will also act as a case study of how such methods could be applied to JLA PSPs for other conditions. The potential benefit of this research is in reducing the first two stages of research waste (i) ‘Questions relevant to research users?’ And (ii) ‘Appropriate research design, conduct and analysis?’ identified by Chalmers and Glasziou (2009). The methods proposed for doing the work will be described, with a focus on those stages already underway. This includes defining the decision problems, building on the work of the eczema JLA PSP, and conceptual modelling, understanding the disease process and service pathways for eczema with expert input. The potential usefulness and challenges of the approach will be discussed. Strengthening methods around research prioritisation and study design is important to ensure value for money from limited research funding. Reference: Chalmers I, Glasziou P. Avoidable waste in the production and reporting of research evidence. Lancet 2009; 374: 86-89

Conference/Value in Health Info

2015-11, ISPOR Europe 2015, Milan, Italy

Value in Health, Vol. 18, No. 7 (November 2015)

Code

PRM271

Topic

Methodological & Statistical Research

Topic Subcategory

Confounding, Selection Bias Correction, Causal Inference

Disease

Sensory System Disorders

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