Bayesian Inference for Prevalence in Longitudinal Two‐Phase Studies

Bayesian Inference for Prevalence in Longitudinal Two‐Phase Studies 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Bayesian Inference for Prevalence in Longitudinal Two‐Phase Studies

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Publisher
Wiley
Copyright
Copyright © 1999 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0006-341X
eISSN
1541-0420
DOI
10.1111/j.0006-341X.1999.01145.x
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

BiometricsWiley

Published: Dec 1, 1999

References

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