Analysis of uncertainty in health care cost-effectiveness studies: an introduction to statistical issues and methods.
Cost-effectiveness analysis is now an integral part of health technology assessment and addresses the question of whether a new treatment or other health care program offers good value for money. In this paper we introduce the basic framework for decision making with cost-effectiveness data and then review recent developments in statistical methods for analysis of uncertainty when cost-effectiveness estimates are based on observed data from a clinical trial. Although much research has focused on methods for calculating confidence intervals for cost-effectiveness ratios using bootstrapping or Fieller's method, these calculations can be problematic with a ratio-based statistic where numerator and/or denominator can be zero. We advocate plotting the joint density of cost and effect differences, together with cumulative density plots known as cost-effectiveness acceptability curves (CEACs) to summarize the overall value-for-money of interventions. We also outline the net-benefit formulation of the cost-effectiveness problem and show that it has particular advantages over the standard incremental cost-effectiveness ratio formulation.
O'Brien
B J
BJ
Department of Clinical Epidemiology and Biostatistics, McMaster University, Canada. obrienb@mcmaster.ca
Briggs
A H
AH
D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003362:Cost-Benefit Analysis / Q000379:methods* / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D006302:Health Services Research / Q000379:methods; D006801:Humans; D017059:Models, Econometric; D013673:Technology Assessment, Biomedical / Q000191:economics / Q000379:methods*