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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Published on: January 8, 2020

Bayesian inference for disease prevalence using negative binomial group testing.

Nicholas A Pritchard1, Joshua M Tebbs

  • 1Department of Mathematics and Statistics, Coastal Carolina University, Conway, SC 29528, USA.

Biometrical Journal. Biometrische Zeitschrift
|January 25, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian approach for estimating disease prevalence using group testing with inverse sampling. New methods were developed and validated using West Nile Virus data.

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Last Updated: Jun 5, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

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Published on: January 8, 2020

Area of Science:

  • Statistics
  • Epidemiology
  • Biostatistics

Background:

  • Group testing (pooled testing) and inverse sampling are common for estimating small proportions.
  • Bayesian methods offer a robust framework for statistical inference.

Purpose of the Study:

  • To develop novel Bayesian estimators for disease prevalence using group testing with inverse sampling.
  • To evaluate the performance of these new estimators using various prior distributions and loss functions.

Main Methods:

  • A Bayesian approach was adopted, integrating prior knowledge of disease incidence.
  • Closed-form expressions for posterior distributions and estimators were derived.
  • Estimators were evaluated using both Bayesian and classical criteria.

Main Results:

  • New point and credible interval estimators for disease prevalence were developed.
  • The proposed methods demonstrated effectiveness in estimating small proportions.
  • The methodology was successfully applied to a real-world dataset.

Conclusions:

  • The developed Bayesian framework provides a valuable tool for disease prevalence estimation in group testing with inverse sampling.
  • The new estimators offer improved accuracy and reliability.
  • The approach is applicable to public health surveillance, such as for West Nile Virus.