Analysis of uncertainty in health care cost-effectiveness studies: an introduction to statistical issues and methods

Stat Methods Med Res. 2002 Dec;11(6):455-68. doi: 10.1191/0962280202sm304ra.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Clinical Trials as Topic / statistics & numerical data
  • Cost-Benefit Analysis / methods*
  • Cost-Benefit Analysis / statistics & numerical data
  • Data Interpretation, Statistical
  • Decision Support Techniques
  • Health Services Research / methods
  • Humans
  • Models, Econometric*
  • Technology Assessment, Biomedical / economics
  • Technology Assessment, Biomedical / methods*