The design and analysis of stochastic cost-effectiveness studies for the evaluation of health care interventions

Many clinical trials are in progress that involve the collection of patient-level data on both the health outcome and resource use consequences of the health care interventions under evaluation. The overall aim of many such evaluations will be to undertake a cost-effectiveness analysis, which will often result in a cost-effectiveness ratio summarizing the value for money of the intervention in question. In this paper we explore the issues surrounding the design and analysis of such studies. At the design stage of an analysis, we propose an improved sample size formula for cost-effectiveness analysis that allows for covariance between cost and effect,differences. This approach is based on the 'net benefits' approach to the analysis of uncertainty in cost-effectiveness analysis. At the analysis stage of an evaluation, we show that the net-benefit approach is equivalent to an appropriate analysis on the cost-effectiveness plane. We introduce the 'cost-effectiveness acceptability curve' as an alternative method for summarizing cost-effectiveness results that directly address the study question of whether an intervention is cost-effective.