Statistical determination of cost‐effectiveness frontier based on net health benefits

Statistical methods are given for producing a cost‐effectiveness frontier for an arbitrary number of programs. In the deterministic case, the net health benefit (NHB) decision rule is optimal; the rule funds the program with the largest positive NHB at each λ , the amount a decision‐maker is willing to pay for an additional unit of effectiveness. For bivariate normally distributed cost and effectiveness variables and a specified λ , a statistical procedure is presented, based on the method of constrained multiple comparisons with the best (CMCB), for determining the program with the largest NHB. A one‐tailed t test is used to determine if the NHB is positive. To obtain a statistical frontier in the λ ‐NHB plane, we develop a method to produce the region in which each program has the largest NHB, by pivoting a CMCB confidence interval. A one‐sided version of Fieller's theorem is used to determine the region where the NHB of each program is positive. At each λ , the pointwise error rate is bounded by a prespecified α . Upper bounds on the familywise error rate, the probability of an error at any value of λ , are given. The methods are applied to a hypothetical clinical trial of antipsychotic agents. Copyright © 2002 John Wiley & Sons, Ltd.