title: Bayesian methods for design and analysis of cost-effectiveness trials in the evaluation of health care technologies.
abstract: We review the development of Bayesian statistical methods for the design and analysis of randomized controlled trials in the assessment of the cost-effectiveness of health care technologies. We place particular emphasis on the benefits of the Bayesian approach; the implications of skew cost data; the need to model the data appropriately to generate efficient and robust inferences instead of relying on distribution-free methods; the importance of making full use of quantitative and structural prior information to produce realistic inferences; and issues in the determination of sample size. Several new examples are presented to illustrate the methods. We conclude with a discussion of the key areas for future research.
authors.affiliation: Centre for Bayesian Statistics in Health Economics, Department of Probability and Statistics, University of Sheffield, UK.
authors.forename: A
authors.initials: A
authors.lastname: O'Hagan
authors.forename: J W
authors.initials: JW
authors.lastname: Stevens
mesh_terms: D001168:Arthritis / Q000628:therapy; D001249:Asthma / Q000188:drug therapy; D001499:Bayes Theorem; D002908:Chronic Disease; D003362:Cost-Benefit Analysis / Q000379:methods* / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D006302:Health Services Research / Q000191:economics / Q000379:methods / Q000706:statistics & numerical data; D006333:Heart Failure / Q000188:drug therapy; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000191:economics / Q000706:statistics & numerical data; D012107:Research Design; D013673:Technology Assessment, Biomedical / Q000191:economics / Q000379:methods*