Assessing and comparing costs: how robust are the bootstrap and methods based on asymptotic normality?
质疑了在卫生经济学中常用于偏态成本数据的自助法和基于渐近正态性的方法，指出即使技术上有效，也可能导致低效甚至误导的推断，强调应使用识别偏态的方法并考虑贝叶斯先验信息。
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.