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Estimation of sensitivity and specificity of multiple repeated binary tests without a gold standard
Chunling Wang1, Timothy E Hanson2
1Department of Statistics, University of South Carolina, Columbia, South Carolina.
Statistics in Medicine
|March 1, 2019
Summary
This study introduces a new statistical model for analyzing multiple diagnostic tests over time without needing gold standard data. The model accounts for test correlations and enables straightforward inference using Markov chain Monte Carlo methods.
Area of Science:
- Biostatistics
- Medical Diagnostics
- Statistical Modeling
Background:
- Accurate diagnostic testing is crucial in healthcare.
- Repeated diagnostic tests over time are common in clinical practice.
- Existing models often require gold standard data, limiting their applicability.
Purpose of the Study:
- To develop a flexible statistical model for multiple diagnostic tests applied repeatedly over time.
- To address the need for models that do not require gold standard data.
- To incorporate correlations among tests within and across time points.
Main Methods:
- A novel statistical model for longitudinal diagnostic test data.
- Identifiability demonstrated with a minimum of three tests.
- Inclusion of correlations within and across time points.
- Markov chain Monte Carlo (MCMC) for posterior inference.
- Availability of sample R code.
Main Results:
- The proposed model is identifiable and robust.
- Demonstrated utility through simulations and real-world scaphoid fracture data analysis.
- Consideration of omnibus tests for parallel and serial test configurations.
Conclusions:
- The developed model provides a powerful tool for analyzing longitudinal diagnostic test data without gold standard requirements.
- The methodology facilitates accurate assessment of diagnostic test performance over time.
- The approach is broadly applicable in various medical diagnostic scenarios.
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