Abstract
Cost-effectiveness analysis studies in education often prioritize descriptive statistics of cost-effectiveness measures, such as the point estimate of the incremental cost-effectiveness ratio (ICER), while neglecting inferential statistics like confidence intervals (CIs). Without CIs, it is impossible to make meaningful comparisons of alternative educational strategies, as there is no basis for assessing the uncertainty of point estimates or the plausible range of ICERs. This study evaluates the relative performance of five methods of constructing CIs for ICERs in randomized controlled trials with cost-effectiveness analyses. We found that the Monte Carlo interval method based on summary statistics consistently performed well regarding coverage, width, and symmetry. It yielded estimates comparable to the percentile bootstrap method across multiple scenarios. In contrast, Fieller’s method did not work well with small samples and small treatment effects. Further, Taylor’s method and the Box method performed least well. We discuss two-sided and one-sided hypothesis testing based on ICER CIs, develop tools for calculating these ICER CIs, and demonstrate the calculation using an empirical example. We conclude with suggestions for applications and extensions of this work.
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