Bayesian inference for prevalence in longitudinal two-phase studies.

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.

journal_name

Biometrics

journal_title

Biometrics

authors

Erkanli A,Soyer R,Costello EJ

doi

10.1111/j.0006-341x.1999.01145.x

subject

Has Abstract

pub_date

1999-12-01 00:00:00

pages

1145-50

issue

4

eissn

0006-341X

issn

1541-0420

journal_volume

55

pub_type

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