Identifying Research Priorities: The Value of Information Associated with Repeat Screening for Age-Related Macular Degeneration

Laura Bojke, Karl Claxton, Mark Sculpher and Stephen Palmer
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Laura Bojke: Centre for Health Economics, kpc1@york.ac.uk, University of York, York, United Kingdom
Karl Claxton: Centre for Health Economics, University of York, York, United Kingdom, Department of Economics
Stephen Palmer: Centre for Health Economics, University of York, York, United Kingdom

Medical Decision Making, 2008, vol. 28, issue 1, 33-43

Abstract: The authors report an analysis that was developed as part of a pilot study examining the use of decision analysis and value-of-information methods to inform research prioritization decisions for the UK health care system. This analysis was conducted to inform decision makers whether additional research on screening for age-related macular degeneration (AMD) would be worthwhile and to demonstrate the benefits and feasibility of using such analytic methods to inform policy decision within the time-lines demanded by existing procedures. A probabilistic decision model was developed to establish the cost-effectiveness of a policy of repeat screening for AMD using the Amsler grid followed by treatment with photodynamic therapy (PDT) compared with 2 alternatives: PDT without screening (self-referral) and no screening or treatment. Screening for AMD appears to be cost-effective on the basis of existing evidence; however, the decision to implement a policy of screening is somewhat uncertain, with a probability that screening is cost-effective of 0.87 and 0.72 for the 20/40 and 20/80 models, respectively, at a threshold of £30,000 per quality-adjusted life-year. The expected value of perfect information (EVPI) associated with this decision is substantial (£6.9 million for the 20/40 model and £14.5 million for the 20/80 model), with a sizeable EVPI associated with the effect of PDT on quality of life. The analysis demonstrates that EVPI analysis can be implemented in a timely fashion to inform the type of research prioritization decisions faced by any health care system. This case study also illustrates the need to account for any structural uncertainties appropriately.

Keywords: decision making; modeling; research prioritization (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:28:y:2008:i:1:p:33-43

DOI: 10.1177/0272989X07309638

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