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Analysis of uncertainty in health care cost-effectiveness studies: an introduction to statistical issues and methods
B J O’Brien
Department of Clinical Epidemiology and Biostatistics, McMaster University, Canada, and Centre for Evaluation of Medicines, St. Joseph’s Hospital, Hamilton, Canada, obrienb{at}mcmaster.ca
A H Briggs
Health Economics Research Centre, Department of Public Health, University of Oxford, Oxford, UK
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.
Statistical Methods in Medical Research, Vol. 11, No. 6,
455-468 (2002)
DOI: 10.1191/0962280202sm304ra
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