An Iterative Bayesian Approach to Health Technology Assessment: Application to a Policy of Preoperative Optimization for Patients Undergoing Major Elective Surgery

Elisabeth Fenwick, Steve Palmer, Karl Claxton, Mark Sculpher, Keith Abrams and Alex Sutton
Additional contact information
Elisabeth Fenwick: Department of Economics and Related Studies, University of York, York, United Kingdom, Centre for Health Economics, University of York, e.fenwick@clinmed.gla.ac.uk
Steve Palmer: Centre for Health Economics, University of York
Karl Claxton: Department of Economics and Related Studies, University of York, York, United Kingdom, Centre for Health Economics, University of York
Mark Sculpher: Centre for Health Economics, University of York
Keith Abrams: Department of Health Sciences, University of Leicester, Leicester, United Kingdom
Alex Sutton: Department of Health Sciences, University of Leicester, Leicester, United Kingdom

Medical Decision Making, 2006, vol. 26, issue 5, 480-496

Abstract: Purpose . This article presents an iterative framework for managing the dynamic process of health technology assessment. The framework uses Bayesian statistical decision theory and value of information (VOI) analysis to inform decision making regarding appropriate patient management and to direct future research effort over the lifetime of a technology. Within the article, the framework is applied to a policy decision regarding preoperative patient management before major elective surgery, for which trial data are available. Method . The evidence available prior to the trial is used to determine the appropriate method of patient management and to ascertain whether, at the time of commissioning, the trial was potentially worthwhile. The prior information is then updated with the trial data via a Bayesian analysis using informative priors. This post trial information set is then used to reassess the appropriate method for patient management and to determine whether there is a requirement for any further research. Results . Prior to the trial, preoperative optimization with dopexamine is identified as the appropriate method of patient management. The results of the VOI analysis suggest that a short-term trial was potentially worthwhile (population expected value of perfect information [EVPI] = £48 million). Following the trial, the uncertainty surrounding the choice of appropriate patient management and the potential worth of further research had increased (population EVPI = £67 million). Conclusions . The article demonstrates the value and practicality of applying the iterative framework to the dynamic process of health technology assessment. It is only by formally incorporating all of the information available to decision makers, through informed priors, that the appropriate decisions can be made.

Keywords: Bayesian analysis; cost-effectiveness analysis; decision analysis; preoperative procedures; technology assessment (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X06290493 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:26:y:2006:i:5:p:480-496

DOI: 10.1177/0272989X06290493

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().