JSM 2004 - Toronto

Abstract #302113

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Activity Number: 16
Type: Topic Contributed
Date/Time: Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #302113
Title: A Test to Detect Space-time Clustering and a Comparison with Some Existing Methods
Author(s): James C. Gear*+ and Dana Quade
Companies: Monsanto Company and University of North Carolina
Address: Statistics Technology Center - O3B, St. Louis, MO, 63167,
Keywords: disease clustering ; space-time clustering
Abstract:

This research introduces a new statistical test for evaluating space-time clustering in data where exact location and time information are available for the points of interest (cases). The test statistic, DP, is defined as the length of the path from X[1] to X[n] when the n cases are ordered by time of occurrence. Significance of the test is most appropriately determined by comparing the directed path length of the data to the empirical distribution of lengths obtained from all possible orderings of the n cases, or a random subset of those orderings when n is large. The properties of this test are investigated using Monte Carlo techniques on clustered and unclustered simulated data. This test is then compared with the commonly used Knox's test and Mantel's Generalized Regression, using Monte Carlo techniques on simulated data. Nearly 20 years of brain cancer data from New Mexico are then examined with these tests, in order to compare the performance of these tests on actual data.

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