Why are bootstrap methods used in economic evaluations?

• A. They always produce normal distributions
• B. Because cost and QoL data are often skewed and violate parametric assumptions
• C. They reduce sample size
• D. They are faster

Answer: (B)

Bootstrapping is a resampling approach used to quantify uncertainty in economic evaluations without assuming a specific parametric distribution for costs and effects. In economic evaluations, cost data are often right-skewed because a few individuals incur very high costs, and QoL measures can be non-normally distributed. This makes traditional parametric methods unreliable or unstable. By repeatedly drawing samples with replacement from the observed data and recalculating the incremental cost and effect in each resample, you build an empirical distribution that reflects the actual variability and the relationship between costs and outcomes. This lets you derive confidence intervals for costs and QALYs and create cost-effectiveness acceptability curves that show the probability an intervention is cost-effective at different willingness-to-pay thresholds. The advantage is robustness to skewness and small sample issues, rather than assuming normality.