Sabine E Grimm1, Simon Dixon2, John W Stevens2. 1. Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, Maastricht, Netherlands (SEG). 2. School of Health and Related Research, University of Sheffield, Sheffield, UK (SD, JWS).
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.
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.
Keywords:
cost utility analysis; decision analysis; implementation dynamics; value of information
Authors: Mathyn Vervaart; Mark Strong; Karl P Claxton; Nicky J Welton; Torbjørn Wisløff; Eline Aas Journal: Med Decis Making Date: 2021-12-30 Impact factor: 2.749