Semi-parametric modelling for costs of health care technologies.

Abstract:

: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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Conigliani C,Tancredi A

doi

10.1002/sim.2012

subject

Has Abstract

pub_date

2005-10-30 00:00:00

pages

3171-84

issue

20

eissn

0277-6715

issn

1097-0258

journal_volume

24

pub_type

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