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Multiple Summary Knowledge-Based Disease Prevalence Estimation for Group Testing Data
Wanshuo Yang1, Juan Ding2, Wenjun Xiong3
1College of Mathematics and Statistics, Chongqing University, Chongqing, China.
Statistics in Medicine
|July 27, 2026
Summary
Group testing efficiently screens for rare diseases. A new method uses external data to improve prevalence estimation, making it more accurate and cost-effective, especially with limited samples.
Area of Science:
- Biostatistics
- Epidemiology
- Statistical Genetics
Background:
- Group testing reduces costs and time for disease screening.
- Parameter estimation can be inefficient with limited group testing data.
- External summary statistics may be available in some scenarios.
Purpose of the Study:
- To improve parameter estimation in group testing using external data.
- To develop a novel multiple-source knowledge transfer method for prevalence estimation.
- To enhance efficiency in rare disease screening.
Main Methods:
- Proposed a multiple-source knowledge transfer method for group testing data.
- Established asymptotic normality for the proposed prevalence estimator.
- Developed a data-driven procedure to identify transferable external data sources.
- Extended the framework to Dorfman's two-stage group testing design.
Main Results:
- The new prevalence estimator is consistent and more efficient than traditional methods.
- Integrating retesting data in two-stage designs yields efficiency gains.
- Theoretical results are supported by simulations and real-data application.
Conclusions:
- The proposed knowledge transfer method enhances group testing efficiency.
- The method provides a statistically sound approach for utilizing external data.
- Demonstrated favorable finite-sample performance for rare disease screening.
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