UNCERTAINTY IN COST-EFFECTIVENESS ANALYSIS

Abstract

Interest is growing in the application of standard statistical inferential techniques to the calculation of cost-effectiveness ratios (CER), but individual level data will not be available in many cases because it is very difficult to undertake prospective controlled trials of many public health interventions. We propose the application of probabilistic uncertainty analysis using Monte Carlo simulations, in combination with nonparametric bootstrapping techniques where appropriate. This paper also discusses how decision makers should interpret the CER of interventions where uncertainty intervals overlap. We show how the incorporation of uncertainty around costs and effects of interventions into a stochastic league table provides additional information to decision makers for priority setting. Stochastic league tables inform decision makers about the probability that a specific intervention would be included in the optimal mix of interventions for different resource levels, given the uncertainty surrounding the interventions.