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ClustR: A Space-Time Cluster Analysis R Package for Individual-level Data.

Catherine Enders1, Rebecca J Hyde1, Steve Selvin1

  • 1From the Division of Epidemiology and Biostatistics, University of California, Berkeley, CA.

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Summary
This summary is machine-generated.

A new R package, ClustR, offers efficient space-time cluster analysis for individual-level disease data. It performs well, especially in urban areas, complementing existing tools like SaTScan.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Computational Biology

Background:

  • Lack of individual-level longitudinal data hindered disease cluster investigation.
  • Development of statistical tools for space-time cluster analysis was needed.

Purpose of the Study:

  • Introduce ClustR, a novel R package for space-time cluster analysis.
  • Evaluate ClustR's performance and compare it with SaTScan.

Main Methods:

  • Developed the ClustR package in R.
  • Evaluated performance using simulated California population data.
  • Compared ClustR against SaTScan for space-time cluster detection.

Main Results:

  • ClustR demonstrated high sensitivity for urban clusters and generally high specificity.
  • ClustR was faster than SaTScan, with comparable sensitivity but lower specificity.
  • Both ClustR and SaTScan showed strengths in detecting different cluster types.

Conclusions:

  • ClustR is a user-friendly, efficient tool for individual-level space-time cluster analysis.
  • ClustR fills a gap in available statistical software for disease cluster investigation.
  • ClustR and SaTScan have complementary strengths and can be used together.