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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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BaySTDetect: detecting unusual temporal patterns in small area data via Bayesian model choice.

Guangquan Li1, Nicky Best, Anna L Hansell

  • 1Department of Epidemiology & Biostatistics, Imperial College, London W2 1PG, UK. guang.li@imperial.ac.uk

Biostatistics (Oxford, England)
|March 29, 2012
PubMed
Summary

BaySTDetect identifies unusual local disease trends using Bayesian model choice. This method aids early detection of public health issues, as seen with chronic obstructive pulmonary disease (COPD) in England.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Small area data modeling is crucial for epidemiology and government statistics.
  • Abrupt changes in local temporal trends can indicate new risk factors or policy impacts.
  • Detecting unusual temporal patterns is vital for public health surveillance and investigation.

Purpose of the Study:

  • To introduce BaySTDetect, a novel Bayesian method for detecting unusual space-time patterns in small area data.
  • To assess the method's performance through simulations and a retrospective case study.
  • To provide a reliable tool for early identification of localized health issues.

Main Methods:

  • Bayesian model choice comparing a common temporal trend model with area-specific trend models.
  • Calculation of posterior probability for each area belonging to the common trend model.
  • Estimation of the false discovery rate (FDR) using a Bayesian approach.

Main Results:

  • BaySTDetect demonstrated consistent performance in simulations for detecting pattern departures and estimating FDR.
  • Retrospective analysis of COPD mortality data identified an unusual district (Tower Hamlets).
  • The method's findings aligned with later recognized higher COPD rates in the identified district.

Conclusions:

  • BaySTDetect is an effective tool for detecting unusual local temporal trends in small area data.
  • The method can facilitate early identification of public health concerns, enabling timely interventions.
  • The study highlights the utility of space-time modeling in public health surveillance and policy evaluation.