Cost-effectiveness inferences from bootstrap quadrant confidence levels: three degrees of dominance.

When with at least 95% confidence a new treatment is shown to be not only less costly (LC), but also more effective (ME), than a current treatment, that new treatment can be said to "strictly dominate" the current treatment statistically. But what can be said when head-to-head treatment comparisons turn out to be less clear-cut than this? Here, we propose two additional sets of specific LC and/or ME confidence thresholds to define the concepts of "some dominance" and "much dominance." Confidence levels associated with entire quadrants of the incremental cost-effectiveness (ICE) plane are easily computed using the same bootstrapping techniques used to estimate an "acceptability curve." Our two proposed additional "degrees" of dominance, although less stringent than strict dominance, are nevertheless more stringent than commonly accepted approaches using ICE ratio or net benefit calculations. To illustrate analysis concepts, we use data from a randomized, double-blind, placebo- and active comparator-controlled clinical registration trial for treatment of major depressive disorder (MDD). As is typical, our case study is rather small and short term, providing outcome information for a total of only 264 patients during their initial 8 weeks of acute-phase MDD treatment. Thus, we focus attention on sensitivity analyses, showing that the bootstrap distribution of cost-effectiveness uncertainty is robust across two alternative ways of measuring overall effectiveness and three alternative ways of imputing missing values. Evaluation of the balance between cost and benefit is particularly difficult when a new pharmacological treatment is first introduced, yet information of this sort is highly desired by decision makers. We show that, even with only a relatively modest amount of clinical trial information, sensitivity analyses can still confirm that cost-effectiveness comparisons are being made in a consistent fashion. In contrast, extensive follow-up comparisons using data from actual clinical practice will almost always ultimately be needed to better inform health policy makers.