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Weight estimation and significance testing for three focused statistics.

Peter A Rogerson¹

¹Departments of Geography and Biostatistics, University at Buffalo, Buffalo, NY 14261, USA. rogerson@buffalo.edu

Statistical Methods in Medical Research

|April 21, 2012

Summary

This study addresses the challenge of multiple testing in spatial cluster detection. It proposes methods to adjust significance levels when analyzing disease incidence data with various spatial weight definitions.

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Area of Science:

Spatial statistics
Epidemiology
Biostatistics

Background:

Focused tests for clustering detect unusual incidence of phenomena in specific locations.
Defining 'around' a location involves assigning weights to surrounding areas, which can lead to varied significance levels.
Trying multiple weight definitions creates a multiple testing problem, potentially yielding false positives.

Purpose of the Study:

To develop and describe approaches for adjusting significance levels in spatial cluster detection when multiple weight definitions are used.
To mitigate the risk of false positives arising from the multiple testing problem in spatial analysis.
To provide statistically sound methods for evaluating spatial clusters under varying definitions of proximity.

Main Methods:

The study describes methods for adjusting significance levels for spatial cluster detection.
Approaches are developed for the local scan statistic, maximum chi-square statistic, and a modified Stone's statistic.
These methods account for multiple significance tests arising from different weight specifications.

Main Results:

The proposed methods provide a way to control the overall Type I error rate when exploring various spatial weight definitions.
Adjusted significance levels allow for more reliable identification of statistically significant spatial clusters.
The methods are illustrated using real-world leukemia data, demonstrating their practical application.

Conclusions:

Adjusting significance levels is crucial when employing multiple spatial weight definitions in cluster detection analysis.
The developed approaches offer a robust framework for spatial epidemiological studies, enhancing the validity of findings.
Accurate identification of disease clusters requires careful consideration and correction for multiple testing inherent in spatial analysis.