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Semi‐parametric modelling for costs of health care technologies
Author(s) -
Conigliani C.,
Tancredi A.
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2012
Subject(s) - skew , generalized pareto distribution , piecewise , computer science , normality , econometrics , parametric statistics , bayesian probability , pareto principle , data transformation , transformation (genetics) , statistics , mathematics , data mining , extreme value theory , telecommunications , mathematical analysis , biochemistry , chemistry , gene , data warehouse
Cost data that arise in the evaluation of health care technologies usually exhibit highly skew, heavy‐tailed and, possibly, multi‐modal distributions. Distribution‐free methods for analysing these data, such as the bootstrap, or those based on the asymptotic normality of sample means, may often lead to inefficient or misleading inferences. On the other hand, parametric models that fit the data (or a transformation of the data) equally well can produce very different answers. We consider a Bayesian approach, and model cost data with a distribution composed of a piecewise constant density up to an unknown endpoint, and a generalized Pareto distribution for the remaining tail. Copyright © 2004 John Wiley & Sons, Ltd.