Citation

Erkanli A, Soyer R, Costello EJ. Biometrics 1999; 55(4): 1145-1150.

Affiliation

Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina 27701, USA. al@psych.mc.duke.edu

Copyright

(Copyright © 1999, Biometric Society, Publisher John Wiley and Sons)

DOI

unavailable

PMID

11315060

Abstract

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.

Language: en