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Related Concept Videos

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Related Experiment Video

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

A hierarchical Bayesian approach to multiple testing in disease mapping.

Dolores Catelan1, Corrado Lagazio, Annibale Biggeri

  • 1Department of Statistics "G.Parenti", University of Florence, Italy. catelan@ds.unifi.it

Biometrical Journal. Biometrische Zeitschrift
|September 3, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian method for multiple testing in disease mapping, using lung cancer mortality data from Italy. The approach identifies areas with divergent risk while controlling for statistical testing, offering a robust disease surveillance tool.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Disease mapping often involves multiple statistical tests, increasing the risk of false positives.
  • Accurate identification of areas with divergent disease risk is crucial for public health interventions.
  • Traditional methods may not adequately control for multiple testing in complex spatial data.

Purpose of the Study:

  • To develop a Bayesian approach for multiple testing in disease mapping.
  • To estimate posterior classification probabilities for identifying non-divergent areas.
  • To explore connections between the proposed Bayesian model and the false discovery rate (FDR) approach.

Main Methods:

  • A tri-level hierarchical Bayesian model was developed.
  • The model estimates the posterior probability of a municipality belonging to the set of non-divergent areas.
  • Poisson-Gamma and Besag, York and Mollié models were considered to handle extra Poisson variability.

Main Results:

  • The Bayesian approach effectively controls for multiple testing in disease mapping.
  • Posterior classification probabilities were used to identify areas with divergent risk.
  • Sensitivity analysis highlighted the influence of prior distributions on posterior inference.

Conclusions:

  • Hierarchical Bayesian models offer a sound framework for modeling classification probabilities in disease mapping.
  • The proposed method provides a robust tool for spatial epidemiology and public health surveillance.
  • The approach allows for the incorporation of subject-specific information through informative prior distributions.