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Related Experiment Video

Updated: Feb 9, 2026

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
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A log-Weibull spatial scan statistic for time to event data.

Iram Usman1, Rhonda J Rosychuk2

  • 1Department of Pediatrics, 3-077, Edmonton Clinic Health Academy, University of Alberta, 11405 87 Avenue NW, Edmonton, AB, T6G 1C9, Canada.

International Journal of Health Geographics
|June 14, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new spatial scan statistic using the log-Weibull distribution to detect geographic clusters in time-to-event data. The method effectively identifies areas with longer or shorter event times, as seen in Alberta

Keywords:
Atrial fibrillation and flutterEmergency departmentLog-Weibull distributionSpatial scan statisticTime to event

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Spatial scan statistics identify geographic disease clusters.
  • These methods can detect spatial variations in time-to-event data.
  • Existing methods often use exponential or Weibull distributions.

Purpose of the Study:

  • To propose the log-Weibull distribution as an alternative for spatial scan statistics in time-to-event analysis.
  • To compare the log-Weibull and Weibull distributions via simulation.
  • To assess the impact of censoring and statistical power.

Main Methods:

  • Developed a spatial scan statistic based on the log-Weibull distribution.
  • Conducted simulation studies to evaluate performance.
  • Applied the method to time-to-specialist visit data for atrial fibrillation and flutter patients in Alberta.

Main Results:

  • The log-Weibull spatial scan statistic performed well for log-Weibull distributed data.
  • Simulation studies investigated type I censoring and power.
  • Analysis of Alberta data revealed longer times to specialist visits in northern regions.

Conclusions:

  • The proposed log-Weibull spatial scan statistic offers a novel approach for spatial cluster detection in time-to-event data.
  • Simulation results support the utility of this new method.
  • The approach is applicable to real-world health outcome data.