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Bayesian regression for group testing data
Christopher S McMahan1, Joshua M Tebbs2, Timothy E Hanson2
1Department of Mathematical Sciences, Clemson University, Clemson, South Carolina 29634, U.S.A.
Biometrics
|April 14, 2017
Summary
This study introduces a Bayesian regression framework for group testing, improving disease status estimation with covariates. The method enhances accuracy by simultaneously estimating assay errors and covariate effects, applicable to various screening scenarios.
Area of Science:
- Biostatistics
- Epidemiology
- Infectious Disease Modeling
Background:
- Group testing pools specimens to detect diseases, but estimating individual disease status with covariates is challenging.
- Existing regression methods for group testing data are often inefficient and rely on strong assumptions about assay accuracy.
Purpose of the Study:
- To propose a general Bayesian regression framework for analyzing group testing data.
- To overcome limitations of previous methods by simultaneously estimating covariate effects and assay accuracy probabilities.
Main Methods:
- Developed a flexible Bayesian regression framework adaptable to any group testing protocol.
- The model accounts for misclassification errors inherent in assay testing.
- The approach can be applied to screening situations with multiple assays.
Main Results:
- The proposed framework efficiently estimates individual disease status and covariate relationships.
- Simultaneous estimation of assay accuracy improves model robustness.
- Demonstrated applicability using chlamydia screening data from Iowa.
Conclusions:
- The novel Bayesian approach offers a more accurate and flexible method for analyzing group testing data.
- This framework enhances disease surveillance and screening efficiency, particularly in public health initiatives.
- User-friendly R code is provided for practical implementation by researchers and practitioners.