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Optimal group testing designs for estimating prevalence with uncertain testing errors.
Shih-Hao Huang1, Mong-Na Lo Huang1, Kerby Shedden2
1National Sun Yat-sen University, Kaohsiung, Taiwan.
Journal of the Royal Statistical Society. Series B, Statistical Methodology
|December 19, 2017
Summary
Optimal group testing designs were developed to accurately estimate trait prevalence, even with uncertain test sensitivity and specificity. The most efficient designs use three group sizes, with specific frequencies depending on whether prevalence alone or all parameters are the focus.
Area of Science:
- Biostatistics
- Epidemiology
- Experimental Design
Background:
- Estimating disease prevalence is crucial for public health.
- Group testing offers a cost-effective method for screening large populations.
- Test accuracy, defined by sensitivity and specificity, can impact prevalence estimates.
Purpose of the Study:
- To develop optimal experimental designs for group testing.
- To estimate trait prevalence using tests with unknown sensitivity and specificity.
- To compare the efficiency of different group testing designs.
Main Methods:
- Application of optimal design theory for approximate designs.
- Construction of locally D- and D-optimal designs.
- Simulation studies and analysis of a Chlamydia prevalence study.
Main Results:
- The most efficient design for estimating prevalence, sensitivity, and specificity uses three equal-frequency group sizes.
- For prevalence-only estimation, optimal designs utilize three unequal-frequency group sizes.
- Proposed designs maintain high efficiency even with moderate misspecification of parameter values.
Conclusions:
- Optimal group testing designs can improve the accuracy of prevalence estimation.
- The choice of design depends on whether the primary goal is to estimate prevalence alone or multiple parameters.
- These methods provide robust strategies for epidemiological studies with imperfect diagnostic tests.
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