No consensus has yet been reached on how to analyse uncertainty in economic evaluation studies where individual patient data are available for costs and health effects. This paper summarises the available results regarding the analysis of uncertainty on the cost-effectiveness plane and argues for using the net-benefit approach when analysing uncertainty in cost-effectiveness studies. The net-benefit approach avoids the interpretation and statistical problems related to the incremental cost effectiveness ratio and implies several advantages. First, traditional statistical methods can be used for confidence-interval estimation and hypothesis testing. Second, calculation of the optimal sample size and the power of the study are facilitated allowing the correlation between costs and effects to vary within and between patient groups. Third, the use of a Bayesian approach to cost-effectiveness analysis is facilitated. Fourth, a formal relation between cost-effectiveness acceptability curves and statistical inference is provided. Finally, the net-benefit approach gives the Fieller's limits of the confidence interval for the incremental cost-effectiveness ratio in the cost-effectiveness plane. Based on these advantages the net-benefit approach should strongly be considered when analysing uncertainty in cost-effectiveness analyses.