title: Assessing and comparing costs: how robust are the bootstrap and methods based on asymptotic normality?
abstract: This article addresses and challenges some common perceptions in the statistical assessment of costs and cost-effectiveness in health economics. Cost data typically exhibit highly skew distributions. Two techniques whose validity does not depend on any specific form of underlying distribution are the bootstrap and methods based on asymptotic normality of sample means. These methods are generally thought to be appropriate for the analysis of cost data. We argue that, even when these methods are technically valid, they may often lead to inefficient and even misleading inferences. It is important to apply methods that recognise the skewness in cost data. We further demonstrate that it may also be important to incorporate relevant prior information in a Bayesian analysis.
authors.affiliation: Department of Probability and Statistics, University of Sheffield, UK.
authors.forename: Anthony
authors.initials: A
authors.lastname: O'Hagan
authors.forename: John W
authors.initials: JW
authors.lastname: Stevens
mesh_terms: D001499:Bayes Theorem; D003362:Cost-Benefit Analysis / Q000379:methods*; D006302:Health Services Research / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D017059:Models, Econometric; D016011:Normal Distribution; D005791:Patient Care / Q000191:economics*; D015203:Reproducibility of Results; D012494:Sampling Studies; D018709:Statistics, Nonparametric; D006113:United Kingdom