Cost-effectiveness analysis evaluates and compares both costs and effects of alternative therapies. We estimated effects (mean mortality rate) and the mean cost per patient for the BNP and control groups. Mean cost was calculated by multiplying each resource use component by the unit cost and summing the results for each patient; we then calculated the mean across all patients. Recent developments in economic methods emphasize the importance of quantifying uncertainty about the incremental cost-effectiveness ratio by examining the joint density of cost and effect differences.15- 17 Nonparametric bootstrap analysis was used to estimate 95% confidence intervals for differences in average costs and for the incremental cost-effectiveness ratios presented (each of these simulations using 5000 bootstrap samples drawn from the original data set), and also to assess the shape of the joint sampling distribution of the differences in average individual costs and effects between the 2 groups.15- 17 Uncertainties surrounding costs, benefits, and cost-effectiveness were represented by confidence ellipses in the “cost-effectiveness plane.”15- 17 The presentation of cost-effectiveness results as cost-effectiveness ratios with 95% confidence interval is inappropriate, since confidence intervals of costs (ie, the numerator of the cost-effectiveness ratio) and effects (ie, the denominator of the cost-effectiveness ratio) are multiplied, and also insufficient, since the interpretation of cost-effectiveness ratios depends on the quadrants of the cost-effectiveness plane into which incremental costs and effects fall.17- 19 For example, in the assessment of a less efficient but cheaper new treatment strategy (represented in the lower left quadrant of the cost-effectiveness plane), a numerically high cost-effectiveness ratio would be favorable, whereas in the assessment of a more expensive but more efficient strategy (upper right quadrant), the opposite is true.17- 19 The remaining quadrants represent situations where the evaluated strategy is more expensive and less effective (dominated; upper left quadrant) or less expensive and more effective (dominant; lower right quadrant). This taken into account by an additional graphic representation of the bootstrapping results in the cost-effectiveness plane, with 95% and 50% confidence ellipses describing their degree of uncertainty. To assess the robustness of the results, sensitivity analyses were performed for changes in the duration of the initial hospitalization, expense for BNP testing, time in the intensive care unit, cost of long-term medication, and rehospitalization days with BNP guidance. The statistical analyses were performed with the SPSS/PC (version 13.0; SPSS Inc, Chicago, Ill) and SAS/PC (version 8.2; SAS Institute Inc, Cary, NC) software packages. A statistical significance level of .05 was used. All data were analyzed on an intention-to-treat basis. Comparisons were made by means of the t test, Mann-Whitney test, Fisher exact test, and χ2 test as appropriate. Costs were compared by bootstrap t tests. All hypothesis testing was 2 tailed. These analyses were prespecified in the BASEL study protocol. The economic analysis was conducted in Swiss francs and then converted to US dollars by using the average actual currency conversion rate during the trial period.