A study on the use of Gumbel approximation with the Bernoulli spatial scan statistic

The Bernoulli version of the spatial scan statistic is a well established method of detecting localised spatial clusters in binary labelled point data, a typical application being the epidemiological case–control study. A recent study suggests the inferential accuracy of several versions of the spat...

Full description

Saved in:
Bibliographic Details
Published in:Statistics in medicine 2013-08, Vol.32 (19), p.3300-3313
Main Authors: Read, S., Bath, P.A., Willett, P., Maheswaran, R.
Format: Article
Language:English
Subjects:
Accuracy
Aged
Aged, 80 and over
Bernoulli
Chronic obstructive pulmonary disease
Cluster Analysis
cluster detection
Computer Simulation
Data Interpretation, Statistical
Epidemiology
False Positive Reactions
Female
Gumbel
Hospitalization - statistics & numerical data
Humans
Male
Medical statistics
Middle Aged
Monte Carlo
Pulmonary Disease, Chronic Obstructive - epidemiology
spatial scan statistic
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The Bernoulli version of the spatial scan statistic is a well established method of detecting localised spatial clusters in binary labelled point data, a typical application being the epidemiological case–control study. A recent study suggests the inferential accuracy of several versions of the spatial scan statistic (principally the Poisson version) can be improved, at little computational cost, by using the Gumbel distribution, a method now available in SaTScanTM (www.satscan.org). We study in detail the effect of this technique when applied to the Bernoulli version and demonstrate that it is highly effective, albeit with some increase in false alarm rates at certain significance thresholds. We explain how this increase is due to the discrete nature of the Bernoulli spatial scan statistic and demonstrate that it can affect even small p‐values. Despite this, we argue that the Gumbel method is actually preferable for very small p‐values. Furthermore, we extend previous research by running benchmark trials on 12 000 synthetic datasets, thus demonstrating that the overall detection capability of the Bernoulli version (i.e. ratio of power to false alarm rate) is not noticeably affected by the use of the Gumbel method. We also provide an example application of the Gumbel method using data on hospital admissions for chronic obstructive pulmonary disease. Copyright © 2013 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.5746