Power comparisons for disease clustering tests

Authors:
Martin Kulldorff;Toshiro Tango;Peter J. Park
Affiliations:
Department of Statistics, University of Connecticut, Storrs, CT and Department of Community Medicine and Health Care, University of Connecticut, 263 Farmington Avenue, Farmington, CT;Division of Theoretical Epidemiology, Department of Epidemiology, The Institute of Public Health, 6-1 Shirokanedai 4 chome, Minato-ku, Tokyo 108, Japan;Department of Biostatistics, Harvard School of Public Health, 655 Huntington Ave, Boston, MA
Venue:
Computational Statistics & Data Analysis
Year:
2003

Citing 1
Cited 13

Random number generators: good ones are hard to find

Communications of the ACM

Algorithms for rapid outbreak detection: a research synthesis

Journal of Biomedical Informatics
Detecting local regions of change in high-dimensional criminal or terrorist point processes

Computational Statistics & Data Analysis
Arbitrarily shaped multiple spatial cluster detection for case event data

Computational Statistics & Data Analysis
Socio-economic Data Analysis with Scan Statistics and Self-organizing Maps

ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Spatial scan statistics in loglinear models

Computational Statistics & Data Analysis
The choice of the number of bins for the M statistic

Computational Statistics & Data Analysis
A Model-Based Scan Statistics for Detecting Geographical Clustering of Disease

ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I
ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization

Computational Statistics & Data Analysis
Bayesian CAR models for syndromic surveillance on multiple data streams: Theory and practice

Information Fusion
Constrained spanning tree algorithms for irregularly-shaped spatial clustering

Computational Statistics & Data Analysis
Processing aggregated data: the location of clusters in health data

Geoinformatica
Clustering and hot spot detection in socio-economic spatio-temporal data

Transactions on Computational Science VI
On the limiting distribution of the spatial scan statistic

Journal of Multivariate Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

Many different methods have been proposed to test for geographical disease clustering, and more generally, for spatial clustering of any type of observations while adjusting for an inhomogeneous background population generating the observations. Despite the many proposed test statistics, there has been few formal comparisons conducted. We present a collection of 1,220,000 simulated benchmark data sets generated under 51 different cluster models and the null hypothesis, to be used for power evaluations. We then use these data sets to compare the power of the spatial scan statistic, the maximized excess events test and the nonparametric M statistic. All have good power, the first having an advantage for localized hot-spot type clusters and the second for global clustering where randomly located cases generate other cases close by. By making the simulated data sets publicly available, new tests can easily be compared with previously evaluated tests by analyzing the same benchmark data.