Premium
Bayesian Inference for Prevalence in Longitudinal Two‐Phase Studies
Author(s) -
Erkanli Alaattin,
Soyer Refik,
Costello Elizabeth J.
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.01145.x
Subject(s) - deviance information criterion , markov chain monte carlo , deviance (statistics) , bayesian probability , statistics , inference , computer science , econometrics , bayesian inference , markov chain , probit model , bayesian information criterion , random effects model , mathematics , artificial intelligence , medicine , meta analysis
Summary. We consider Bayesian inference and model selection for prevalence estimation using a longitudinal two‐phase design in which subjects initially receive a low‐cost screening test followed by an expensive diagnostic test conducted on several occasions. The change in the subject's diagnostic probability over time is described using four mixed‐effects probit models in which the subject‐specific effects are captured by latent variables. The computations are performed using Markov chain Monte Carlo methods. These models are then compared using the deviance information criterion. The methodology is illustrated with an analysis of alcohol and drug use in adolescents using data from the Great Smoky Mountains Study.