Bayesian and classical estimation of mixed logit: An application to genetic testing

Dean A. Regier, Mandy Ryan, Euan Phimister, Carlo A. Marra

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)


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 Call 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.

Original languageEnglish
Pages (from-to)598-610
Number of pages13
JournalJournal of Health Economics
Issue number3
Early online date3 Dec 2008
Publication statusPublished - May 2009


  • genetic testing
  • discrete choice experiments
  • Hierarchical Bayes
  • maximum simulated likelihood estimation
  • discrete-choice experiments
  • chain monte-carlo
  • mental-retardation
  • developmental delay
  • models
  • heterogeneity
  • preferences
  • health
  • asthma


Dive into the research topics of 'Bayesian and classical estimation of mixed logit: An application to genetic testing'. Together they form a unique fingerprint.

Cite this