Thinking outside the box: recent advances in the analysis and presentation of uncertainty in cost-effectiveness studies.
As many more clinical trials collect economic information within their study design, so health economics analysts are increasingly working with patient-level data on both costs and effects. In this paper, we review recent advances in the use of statistical methods for economic analysis of information collected alongside clinical trials. In particular, we focus on the handling and presentation of uncertainty, including the importance of estimation rather than hypothesis testing, the use of the net-benefit statistic, and the presentation of cost-effectiveness acceptability curves. We also discuss the appropriate sample size calculations for cost-effectiveness analysis at the design stage of a study. Finally, we outline some of the challenges for future research in this area-particularly in relation to the appropriate use of Bayesian methods and methods for analyzing costs that are typically skewed and often incomplete.
Briggs
Andrew H
AH
Health Economics Research Centre, Department of Public Health, University of Oxford, Oxford OX3 7LF, United Kingdom. andrew.briggs@ihs.ox.ac.uk.
O'Brien
Bernie J
BJ
Blackhouse
Gordon
G
D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000191:economics; D003362:Cost-Benefit Analysis / Q000379:methods* / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D006801:Humans; D035501:Uncertainty