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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

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Local multiplicity adjustments for spatial cluster detection.

Ronald E Gangnon1

  • 1Departments of Biostatistics and Medical Informatics and Population Health Sciences, 603 WARF Office Building, University of Wisconsin-Madison, 610 Walnut Street, Madison, WI 53726, USA, ronald@biostat.wisc.edu.

Environmental and Ecological Statistics
|May 21, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces new methods to adjust for multiple comparisons in spatial cluster detection, improving accuracy in areas with varying cluster densities. These adjustments enhance the reliability of identifying disease clusters in diverse geographic settings.

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Area of Science:

  • Biostatistics
  • Spatial Epidemiology
  • Geographic Information Systems (GIS)

Background:

  • The spatial scan statistic is a common method for detecting disease clusters.
  • It uses Monte Carlo simulation to address the multiplicity of comparisons.
  • However, it does not account for local variations in multiplicity, which differ between urban and rural areas.

Purpose of the Study:

  • To propose and evaluate novel spatially-varying multiplicity adjustments for spatial cluster detection.
  • To address the limitations of the standard spatial scan statistic in regions with heterogeneous cluster overlap.

Main Methods:

  • Developed two new adjustments: a nested Bonferroni adjustment and a local averaging adjustment.
  • Conducted simulation studies to assess geographic variations in statistical power.
  • Applied the methods to the New York leukemia dataset and a Wisconsin breast cancer case-control study.

Main Results:

  • Simulation studies revealed geographic variations in the power of the spatial scan statistic and the new methods.
  • The proposed adjustments demonstrated potential for improved cluster detection in areas with varying multiplicity.

Conclusions:

  • The new spatially-varying multiplicity adjustments offer a more nuanced approach to spatial cluster detection.
  • These methods can enhance the accuracy and reliability of identifying disease clusters in diverse geographic contexts.