Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Estimating prevalence by group testing using generalized linear models.

C P Farrington1

  • 1PHLS Communicable Disease Surveillance Centre, London, U.K.

Statistics in Medicine
|September 15, 1992
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Estimation of vaccine effectiveness using the screening method.

International journal of epidemiology·2022
Same author

Is it appropriate to use fixed assay cut-offs for estimating seroprevalence?

Epidemiology and infection·2015
Same author

Use of the self-controlled case-series method in vaccine safety studies: review and recommendations for best practice.

Epidemiology and infection·2011
Same author

Epidemiological studies of the non-specific effects of vaccines: II--methodological issues in the design and analysis of cohort studies.

Tropical medicine & international health : TM & IH·2009
Same author

Measures of disassortativeness and their application to directly transmitted infections.

Biometrical journal. Biometrische Zeitschrift·2009
Same author

Hepatitis B vaccination and first central nervous system demyelinating events: reanalysis of a case-control study using the self-controlled case series method.

Vaccine·2007

This study introduces a generalized linear model for group testing to estimate prevalence. The method simplifies data analysis and corrects for overdispersion, as shown in salmonella and yellow fever virus studies.

Area of Science:

  • Statistics
  • Epidemiology
  • Bioinformatics

Background:

  • Group testing is an efficient method for estimating disease prevalence in large populations.
  • Traditional methods for analyzing group testing data can be complex and computationally intensive.
  • Overdispersion, where variance exceeds the mean, is common in prevalence data and requires specialized statistical handling.

Purpose of the Study:

  • To develop a straightforward method for estimating prevalence using group testing data.
  • To apply generalized linear models (GLMs) for analyzing group testing results.
  • To incorporate existing statistical techniques for overdispersion correction within the GLM framework for group testing.

Main Methods:

  • Utilized generalized linear models (GLMs) to model group testing data.

Related Experiment Videos

  • Applied quasi-likelihood methods to address overdispersion in the prevalence estimates.
  • Validated the proposed methodology through simulations and real-world case studies.

    • Main Results:

    • The GLM approach provides a simple and effective way to analyze group testing data.
    • The method successfully estimated prevalence in two distinct biological contexts: Salmonella contamination in eggs and Yellow Fever Virus infection in mosquitoes.
    • The quasi-likelihood adjustments effectively corrected for overdispersion, leading to more accurate prevalence estimations.

    Conclusions:

    • Generalized linear models offer a powerful and accessible tool for prevalence estimation in group testing.
    • The described method is broadly applicable to various public health and agricultural surveillance scenarios.
    • This approach facilitates the use of standard statistical software for analyzing complex group testing data.