Bayesian methods for design and analysis of cost-effectiveness trials in the evaluation of health care technologies.
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.
O'Hagan
A
A
Centre for Bayesian Statistics in Health Economics, Department of Probability and Statistics, University of Sheffield, UK.
Stevens
J W
JW
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*