Estimating confidence intervals for cost-effectiveness ratios: an example from a randomized trial.
Cost-effectiveness ratios usually appear as point estimates without confidence intervals, since the numerator and denominator are both stochastic and one cannot estimate the variance of the estimator exactly. The recent literature, however, stresses the importance of presenting confidence intervals for cost-effectiveness ratios in the analysis of health care programmes. This paper compares the use of several methods to obtain confidence intervals for the cost-effectiveness of a randomized intervention to increase the use of Medicaid's Early and Periodic Screening, Diagnosis and Treatment (EPSDT) programme. Comparisons of the intervals show that methods that account for skewness in the distribution of the ratio estimator may be substantially preferable in practice to methods that assume the cost-effectiveness ratio estimator is normally distributed. We show that non-parametric bootstrap methods that are mathematically less complex but computationally more rigorous result in confidence intervals that are similar to the intervals from a parametric method that adjusts for skewness in the distribution of the ratio. The analyses also show that the modest sample sizes needed to detect statistically significant effects in a randomized trial may result in confidence intervals for estimates of cost-effectiveness that are much wider than the boundaries obtained from deterministic sensitivity analyses.
Chaudhary
M A
MA
Department of Biostatistics, University of North Carolina at Chapel Hill, NC 27599-7400, USA.
Stearns
S C
SC
D000704:Analysis of Variance; D015982:Bias; D002648:Child; D002662:Child Health Services / Q000706:statistics & numerical data; D016001:Confidence Intervals; D003362:Cost-Benefit Analysis; D006801:Humans; D008403:Mass Screening / Q000706:statistics & numerical data; D008484:Medicaid; D016032:Randomized Controlled Trials as Topic; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric; D013269:Stochastic Processes; D014481:United States