Please use this identifier to cite or link to this item: https://doi.org/10.1002/sim.4082
Title: Log-linear, logistic model fitting and local score statistics for cluster detection with covariate adjustments
Authors: Chan, H.P. 
Tu, I.
Keywords: Cluster detection
Local score statistics
Log-linear model
Logistic model
Scan statistic
Issue Date: 15-Jan-2011
Citation: Chan, H.P., Tu, I. (2011-01-15). Log-linear, logistic model fitting and local score statistics for cluster detection with covariate adjustments. Statistics in Medicine 30 (1) : 91-100. ScholarBank@NUS Repository. https://doi.org/10.1002/sim.4082
Abstract: The standard method for p-value computation of spatial scan statistics, with adjustments for covariate effects, is to conduct Monte Carlo simulations with these effects estimated under the null hypothesis of no clustering. However when the covariates are geographically unbalanced, the proposed Monte Carlo p-value estimates are too conservative, with corresponding loss of power, due to excessive adjustments for confounding between covariates and location. We show that the use of an alternative procedure that involves local score statistics, with parameters fitted on a log-linear or logistic model, addresses this problem. We also discuss extensions of the procedure when there are multiple or continuous covariates. Copyright © 2010 John Wiley & Sons, Ltd.
Source Title: Statistics in Medicine
URI: http://scholarbank.nus.edu.sg/handle/10635/105209
ISSN: 02776715
DOI: 10.1002/sim.4082
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