title: An iterative Bayesian approach to health technology assessment: application to a policy of preoperative optimization for patients undergoing major elective surgery.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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 pounds sterling). 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 pounds sterling).
  
  
  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.
authors.affiliation: Department of Economics and Related Studies, University of York, York, United Kingdom. e.fenwick@clinmed.gla.ac.uk
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Fenwick
authors.forename: Steve
authors.initials: S
authors.lastname: Palmer
authors.forename: Karl
authors.initials: K
authors.lastname: Claxton
authors.forename: Mark
authors.initials: M
authors.lastname: Sculpher
authors.forename: Keith
authors.initials: K
authors.lastname: Abrams
authors.forename: Alex
authors.initials: A
authors.lastname: Sutton
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D003362:Cost-Benefit Analysis; D003661:Decision Support Techniques; D017558:Elective Surgical Procedures / Q000191:economics / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care / Q000191:economics / Q000379:methods*; D011300:Preoperative Care / Q000191:economics / Q000379:methods; D040242:Risk Reduction Behavior; D016019:Survival Analysis