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The efficient design of Nested Group Testing algorithms for disease identification in clustered data.

Ana F Best1, Yaakov Malinovsky2, Paul S Albert3

  • 1Biostatistics Branch, Biometrics Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

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Summary
This summary is machine-generated.

Group testing can reduce disease screening costs. Accounting for clustered data, like geographic HIV prevalence, improves efficiency for higher prevalence diseases with significant between-cluster differences.

Keywords:
clustered datadisease identificationgroup testingpooled sample analysisprevalence heterogeneity

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Public Health

Background:

  • Group testing methods have historically reduced screening costs for rare diseases.
  • Limited research exists on optimizing group testing for clustered disease prevalence data, such as geographic variations in HIV.
  • Efficient group testing designs are crucial for cost-effective disease surveillance and screening.

Purpose of the Study:

  • To evaluate group testing designs that incorporate disease prevalence estimation within clustered populations.
  • To determine optimal group sizes and testing strategies based on estimated prevalence to minimize average tests per subject.
  • To compare different approaches for estimating prevalence (cluster-specific vs. common) and group construction strategies.

Main Methods:

  • Prevalence estimation using individual testing on fixed-size subsets within each cluster.
  • Application of group testing algorithms informed by estimated prevalence to select optimal group sizes.
  • Comparison of designs considering cluster-specific vs. common prevalence, different group testing algorithms, intra- and inter-cluster grouping, and misclassification.
  • Evaluation of practical applications using HIV carrier identification and anti-cancer compound screening examples.

Main Results:

  • For low-prevalence diseases, accounting for clustering in group testing designs offers no significant advantage.
  • For diseases with higher prevalence and substantial between-cluster heterogeneity, incorporating clustering into study design significantly improves efficiency.
  • The choice of prevalence estimation (cluster-specific or common) and group construction impacts overall efficiency.

Conclusions:

  • Group testing designs should account for population clustering when dealing with diseases of higher prevalence and significant heterogeneity.
  • The study provides practical recommendations for optimizing group testing strategies in clustered populations, demonstrated by real-world examples.
  • Tailoring group testing approaches to disease prevalence and population structure enhances screening efficiency and cost-effectiveness.