Document Detail


Bayesian and classical estimation of mixed logit: An application to genetic testing.
MedLine Citation:
PMID:  19345433     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Discrete choice experiments (DCEs) in health economics have recently used the mixed logit (MXL) model to incorporate preference heterogeneity. These studies typically use a classical approach to estimation or have specified normal distributions for the attributes. Specifying normal distributions can lead to erroneous interpretation; non-normal distributions may cause problems with convergence to the global maximum of the simulated log-likelihood function. Hierarchical Bayes (HB) of MXL is an alternative estimation approach that may alleviate problems of convergence. We investigated Bayesian and classical approaches to MXL estimation using a DCE that elicited preferences for a genetic technology. The classical approach produced unrealistic results in one of the econometric specifications, which led to an erroneous willingness to pay estimate. The HB procedure produced reasonable results for both specifications and helped ascertain that the classical procedures were converging at a local maximum.
Authors:
Dean A Regier; Mandy Ryan; Euan Phimister; Carlo A Marra
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2008-12-03
Journal Detail:
Title:  Journal of health economics     Volume:  28     ISSN:  0167-6296     ISO Abbreviation:  J Health Econ     Publication Date:  2009 May 
Date Detail:
Created Date:  2009-05-12     Completed Date:  2009-07-28     Revised Date:  2009-11-19    
Medline Journal Info:
Nlm Unique ID:  8410622     Medline TA:  J Health Econ     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  598-610     Citation Subset:  H    
Affiliation:
National Perinatal Epidemiology Unit, University of Oxford, Old Road Campus, Oxford, England OX3 7LF, United Kingdom. dean.regier@npeu.ox.ac.uk
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MeSH Terms
Descriptor/Qualifier:
Bayes Theorem*
Female
Genetic Testing / statistics & numerical data*
Humans
Logistic Models*
Male
Middle Aged
Models, Econometric

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