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Research prioritization based on expected value of partial perfect information: a case‐study on interventions to increase uptake of breast cancer screening

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  • N. J. Welton
  • A. E. Ades
  • D. M. Caldwell
  • T. J. Peters

Abstract

Summary. We investigate whether Bayesian decision theory, in the form of expected value of partial perfect information (EVPPI) analysis, is a realistic and practical approach to research prioritization. We develop a simple cost‐effectiveness analysis of breast cancer screening as a typical case‐study, motivated by data from a cluster randomized 2 × 2 factorial trial of interventions to increase uptake. An EVPPI analysis is developed which shows that, on the basis of the evidence that was available beforehand, the trial was cost effective, but that after incorporating the results of the trial it would still be cost effective to carry out research that further reduced decision uncertainty. We identify key conceptual and technical issues: the relationship between the target interventions and the previous evidence, the distinction between variation and uncertainty and methods for correlated parameters. EVPPI methods have clear advantages over current methods of research prioritization, but we suggest that some specific sensitivity analyses are required before they can be used confidently in practice. These have limitations, and there is a need to develop robust methods to optimize research portfolios.

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  • N. J. Welton & A. E. Ades & D. M. Caldwell & T. J. Peters, 2008. "Research prioritization based on expected value of partial perfect information: a case‐study on interventions to increase uptake of breast cancer screening," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 807-841, October.
    • Handle: RePEc:bla:jorssa:v:171:y:2008:i:4:p:807-841
    DOI: 10.1111/j.1467-985X.2008.00558.x
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    Cited by:

    1. Jason Madan & Anthony E. Ades & Malcolm Price & Kathryn Maitland & Julie Jemutai & Paul Revill & Nicky J. Welton, 2014. "Strategies for Efficient Computation of the Expected Value of Partial Perfect Information," Medical Decision Making, , vol. 34(3), pages 327-342, April.
    2. Sofia Dias & Nicky J. Welton & Alex J. Sutton & A. E. Ades, 2013. "Evidence Synthesis for Decision Making 5," Medical Decision Making, , vol. 33(5), pages 657-670, July.
    3. Nicky Welton & A. E. Ades, 2012. "Research Decisions In The Face Of Heterogeneity: What Can A New Study Tell Us?," Health Economics, John Wiley & Sons, Ltd., vol. 21(10), pages 1196-1200, October.
    4. Andrew R. Willan & Simon Eckermann, 2012. "Accounting For Between‐Study Variation In Incremental Net Benefit In Value Of Information Methodology," Health Economics, John Wiley & Sons, Ltd., vol. 21(10), pages 1183-1195, October.
    5. Jeffrey, Scott R. & Pannell, David J., 2013. "Economics of Prioritising Environmental Research: An Expected Value of Partial Perfect Information (EVPPI) Framework," Working Papers 144944, University of Western Australia, School of Agricultural and Resource Economics.
    6. Andrew Willan & Simon Eckermann, 2012. "Value of Information and Pricing New Healthcare Interventions," PharmacoEconomics, Springer, vol. 30(6), pages 447-459, June.
    7. Nicky J. Welton & Jason J. Madan & Deborah M. Caldwell & Tim J. Peters & Anthony E. Ades, 2014. "Expected Value of Sample Information for Multi-Arm Cluster Randomized Trials with Binary Outcomes," Medical Decision Making, , vol. 34(3), pages 352-365, April.
    8. Christopher H. Jackson & Simon G. Thompson & Linda D. Sharples, 2009. "Accounting for uncertainty in health economic decision models by using model averaging," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 383-404, April.

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