Non-parametric methods for cost-effectiveness analysis: the central limit theorem and the bootstrap compared

Health Econ. 2010 Mar;19(3):316-33. doi: 10.1002/hec.1477.


Cost-effectiveness analyses (CEA) alongside randomised controlled trials commonly estimate incremental net benefits (INB), with 95% confidence intervals, and compute cost-effectiveness acceptability curves and confidence ellipses. Two alternative non-parametric methods for estimating INB are to apply the central limit theorem (CLT) or to use the non-parametric bootstrap method, although it is unclear which method is preferable. This paper describes the statistical rationale underlying each of these methods and illustrates their application with a trial-based CEA. It compares the sampling uncertainty from using either technique in a Monte Carlo simulation. The experiments are repeated varying the sample size and the skewness of costs in the population. The results showed that, even when data were highly skewed, both methods accurately estimated the true standard errors (SEs) when sample sizes were moderate to large (n>50), and also gave good estimates for small data sets with low skewness. However, when sample sizes were relatively small and the data highly skewed, using the CLT rather than the bootstrap led to slightly more accurate SEs. We conclude that while in general using either method is appropriate, the CLT is easier to implement, and provides SEs that are at least as accurate as the bootstrap.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Confidence Intervals
  • Cost-Benefit Analysis / methods*
  • Costs and Cost Analysis / methods
  • Humans
  • Models, Economic
  • Monte Carlo Method
  • Sample Size
  • Uncertainty