JSM 2005 - Toronto

Abstract #304600

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Activity Number: 403
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #304600
Title: Detecting Spatial Clustering in Matched-case Control Studies
Author(s): Andrea Cook*+ and Yi Li
Companies: Harvard School of Public Health and Dana-Farber Cancer Institute, Harvard School of Public Health
Address: 655 Huntington Avenue, Boston, MA, 02115, United States
Keywords: Cluster Detection ; Spatial Scan ; Cumulative Residual ; Petrochemicals
Abstract:

With the rapid development of the GIS technology, numerous methods have been proposed for detecting spatial clustering, such as the spatial scan statistic (Kulldorff 1997), Maximized Excess Events Test (MEET) (Tango 2000), Besag-Newell's R (Besag and Newell 1991), k-Nearest Neighbors (Cuzick and Edwards 1990), and the M statistic (Bonetti and Pagano 2001). However, none has dealt with spatial data where part of the dependence is due to study design (e.g., matched-pair studies). This paper proposes an extension of the spatial scan statistic that detects spatial clustering when the data are collected through a matched-case-control mechanism. We further propose a robust clustering detection method, specifically a cumulative geographic residual test that allows for discrete outcomes, matched or unmatched. Power comparisons between the spatial scan statistic and cumulative geographic residual test are made via simulations. Utilization of these methods is illustrated by a matched-case-control study investigating the impact of petrochemical exposure on childhood brain and leukemia cancers.

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Revised March 2005