Bayesian inference for prevalence in longitudinal two-phase studies

Biometrics
A ErkanliE J Costello

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.

References

Mar 1, 1995·Statistical Methods in Medical Research·A PicklesJ L Vázquez-Barquero
Sep 1, 1995·Statistical Methods in Medical Research·D J Hand
Dec 1, 1996·Archives of General Psychiatry·E J CostelloD L Tweed
Dec 1, 1996·Statistical Methods in Medical Research·A Gelman, D B Rubin
May 30, 1997·Statistics in Medicine·A ErkanliD Stangl

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Citations

Dec 8, 2004·Preventive Veterinary Medicine·A J BranscumW O Johnson
Sep 22, 2005·Journal of the American Academy of Child and Adolescent Psychiatry·E Jane CostelloAdrian Angold

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