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 Medjournal_title
Statistics in medicineauthors
Conigliani C,Tancredi Adoi
10.1002/sim.2012subject
Has Abstractpub_date
2005-10-30 00:00:00pages
3171-84issue
20eissn
0277-6715issn
1097-0258journal_volume
24pub_type
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