Sample size determination for cost-effectiveness trials

Methods for determining sample size requirements for cost-effectiveness studies are reviewed and illustrated. Traditional methods based on tests of hypothesis and power arguments are given for the incremental cost-effectiveness ratio and incremental net benefit (INB). In addition, a full Bayesian approach using decision theory to determine optimal sample size is given for INB. The full Bayesian approach, based on the value of information, is proposed in reaction to concerns that traditional methods rely on arbitrarily chosen error probabilities and an ill-defined notion of the smallest clinically important difference. Furthermore, the results of cost-effectiveness studies are used for decision making (e.g. should a new intervention be adopted or the old one retained), and employing decision theory, which permits optimal use of current information and the optimal design of new studies, provides a more consistent approach.