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Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin 53726, USA. ronald@biostat.wisc.edu
Biometrics
|July 19, 2011
Summary
This study introduces new methods to improve spatial scan statistics for cluster detection by accounting for local multiplicity variations. These adjusted methods offer better power and unbiased cluster identification in spatial analysis.
Area of Science:
- Epidemiology
- Biostatistics
- Spatial Analysis
Background:
- The spatial scan statistic is crucial for cluster detection but doesn't account for varying local multiplicity.
- Urban areas have more overlapping clusters than rural areas, affecting statistical significance.
- Existing methods like Bonferroni correction have limitations in addressing this variation.
Purpose of the Study:
- To address the limitations of the standard spatial scan statistic regarding local multiplicity variation.
- To propose and evaluate novel adjustments for the spatial scan statistic.
- To compare the performance of adjusted statistics against the standard method and a previously proposed adjustment.
Main Methods:
- Described a local multiplicity adjustment using a nested Bonferroni correction.
- Proposed a novel adjustment using a Gumbel distribution approximation for local scan statistics.
- Compared the power and unbiased cluster detection criteria of three methods.
Main Results:
- The novel Gumbel distribution approximation and the nested Bonferroni correction showed improved performance.
- These adjusted methods demonstrated enhanced power and unbiasedness in cluster detection.
- Application to New York leukemia and Wisconsin breast cancer datasets illustrated practical utility.
Conclusions:
- Adjustments for local multiplicity significantly improve the spatial scan statistic's performance.
- The Gumbel distribution approximation offers a promising novel approach for cluster detection.
- These refined methods enhance the reliability of identifying disease clusters in epidemiological studies.
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