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Bayesian inference for prevalence in longitudinal two-phase studies.

A Erkanli¹, R Soyer, E J Costello

¹Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina 27701, USA. al@psych.mc.duke.edu

|April 21, 2001

Summary

This study introduces Bayesian methods for estimating disease prevalence using longitudinal two-phase designs. The research provides a framework for analyzing changes in diagnostic probability over time in adolescent substance use.

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Area of Science:

Biostatistics
Longitudinal Data Analysis
Bayesian Inference

Background:

Longitudinal two-phase designs are crucial for accurate prevalence estimation, combining initial screening with follow-up diagnostic testing.
Estimating prevalence over time requires sophisticated statistical models to account for changing probabilities and individual variations.

Purpose of the Study:

To develop and compare Bayesian models for prevalence estimation in longitudinal two-phase studies.
To analyze the temporal dynamics of diagnostic probability using mixed-effects probit models.
To illustrate the methodology with adolescent alcohol and drug use data.

Main Methods:

Utilized a longitudinal two-phase design with initial screening and repeated diagnostic tests.
Employed four mixed-effects probit models incorporating latent variables for subject-specific effects.
Performed computations using Markov chain Monte Carlo (MCMC) methods.
Compared models using the deviance information criterion (DIC).

Main Results:

The study successfully applied Bayesian inference and model selection techniques to a complex longitudinal dataset.
Mixed-effects probit models effectively captured changes in diagnostic probability over time.
The deviance information criterion facilitated robust model comparison for prevalence estimation.

Conclusions:

The proposed Bayesian framework offers a powerful approach for prevalence estimation in longitudinal studies.
The methodology is well-suited for analyzing dynamic health behaviors, such as adolescent substance use.
This research contributes to improved statistical methods for public health surveillance and intervention studies.