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Assessing the Expected Value of Research Studies in Reducing Uncertainty and Improving Implementation Dynamics

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  • Sabine E. Grimm
  • Simon Dixon
  • John W. Stevens

Abstract

Background. With low implementation of cost-effective health technologies being a problem in many health systems, it is worth considering the potential effects of research on implementation at the time of health technology assessment. Meaningful and realistic implementation estimates must be of dynamic nature. Objective. To extend existing methods for assessing the value of research studies in terms of both reduction of uncertainty and improvement in implementation by considering diffusion based on expert beliefs with and without further research conditional on the strength of evidence. Methods . We use expected value of sample information and expected value of specific implementation measure concepts accounting for the effects of specific research studies on implementation and the reduction of uncertainty. Diffusion theory and elicitation of expert beliefs about the shape of diffusion curves inform implementation dynamics. We illustrate use of the resulting dynamic expected value of research in a preterm birth screening technology and results are compared with those from a static analysis. Results. Allowing for diffusion based on expert beliefs had a significant impact on the expected value of research in the case study, suggesting that mistakes are made where static implementation levels are assumed. Incorporating the effects of research on implementation resulted in an increase in the expected value of research compared to the expected value of sample information alone. Conclusions. Assessing the expected value of research in reducing uncertainty and improving implementation dynamics has the potential to complement currently used analyses in health technology assessments, especially in recommendations for further research. The combination of expected value of research, diffusion theory, and elicitation described in this article is an important addition to the existing methods of health technology assessment.

Suggested Citation

  • Sabine E. Grimm & Simon Dixon & John W. Stevens, 2017. "Assessing the Expected Value of Research Studies in Reducing Uncertainty and Improving Implementation Dynamics," Medical Decision Making, , vol. 37(5), pages 523-533, July.
    • Handle: RePEc:sae:medema:v:37:y:2017:i:5:p:523-533
    DOI: 10.1177/0272989X16686766
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    1. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    2. Simon Walker & Mark Sculpher & Karl Claxton & Steve Palmer, 2012. "Coverage with evidence development, only in research, risk sharing or patient access scheme? A framework for coverage decisions," Working Papers 077cherp, Centre for Health Economics, University of York.
    3. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629, Decembrie.
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    Cited by:

    1. Mathyn Vervaart & Mark Strong & Karl P. Claxton & Nicky J. Welton & Torbjørn Wisløff & Eline Aas, 2022. "An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial," Medical Decision Making, , vol. 42(5), pages 612-625, July.
    2. Anna Heath, 2022. "Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation," Medical Decision Making, , vol. 42(5), pages 626-636, July.

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