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Activity Number: 25
Type: Contributed
Date/Time: Sunday, August 3, 2008 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #300539
Title: A Generalized Linear Models Approach to Spatial Scan Statistics for Covariate Adjustment
Author(s): Inkyung Jung*+
Companies: The University of Texas Health Science Center at San Antonio
Address: 7703 Floyd Curl Drive, San Antonio, TX, 78229,
Keywords: cluster detection ; confounding factor ; geographical disease surveillance ; GLM
Abstract:

The spatial scan statistic proposed by Kulldorff (1997) is one of the most widely used methods for detecting spatial clusters and evaluating their statistical significance. However, it is not fully capable of adjusting for all types of confounding covariates. In this article, a generalized linear models (GLM) approach to construct spatial scan statistics, which is readily in a form for covariate adjustment, is proposed. Using GLM, spatial scan statistics for different probability models can be formulated in a single framework. The test statistic is based on the log-likelihood ratio test and evaluated using Monte Carlo hypothesis testing. The proposed method is illustrated using Texas female breast cancer data concerning late versus early stage cancer cases with covariates of race/ethnicity and age group.

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Revised September, 2008