The aim of this paper is to discuss the use of Bayesian methods in cost-effectiveness analysis (CEA) and the common ground between Bayesian and traditional frequentist approaches. A further aim is to explore the use of the net benefit statistic and its advantages over the incremental cost-effectiveness ratio (ICER) statistic. In particular, the use of cost-effectiveness acceptability curves is examined as a device for presenting the implications of uncertainty in a CEA to decision makers. Although it is argued that the interpretation of such curves as the probability that an intervention is cost-effective given the data requires a Bayesian approach, this should generate no misgivings for the frequentist. Furthermore, cost-effectiveness acceptability curves estimated using the net benefit statistic are exactly equivalent to those estimated from an appropriate analysis of ICERs on the cost-effectiveness plane. The principles examined in this paper are illustrated by application to the cost-effectiveness of blood pressure control in the U.K. Prospective Diabetes Study (UKPDS 40). Due to a lack of good-quality prior information on the cost and effectiveness of blood pressure control in diabetes, a Bayesian analysis assuming an uninformative prior is argued to be most appropriate. This generates exactly the same cost-effectiveness results as a standard frequentist analysis.