Document Detail


Inference methods for the conditional logistic regression model with longitudinal data.
MedLine Citation:
PMID:  17849385     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This paper considers inference methods for case-control logistic regression in longitudinal setups. The motivation is provided by an analysis of plains bison spatial location as a function of habitat heterogeneity. The sampling is done according to a longitudinal matched case-control design in which, at certain time points, exactly one case, the actual location of an animal, is matched to a number of controls, the alternative locations that could have been reached. We develop inference methods for the conditional logistic regression model in this setup, which can be formulated within a generalized estimating equation (GEE) framework. This permits the use of statistical techniques developed for GEE-based inference, such as robust variance estimators and model selection criteria adapted for non-independent data. The performance of the methods is investigated in a simulation study and illustrated with the bison data analysis.
Authors:
Radu V Craiu; Thierry Duchesne; Daniel Fortin
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Biometrical journal. Biometrische Zeitschrift     Volume:  50     ISSN:  1521-4036     ISO Abbreviation:  Biom J     Publication Date:  2008 Feb 
Date Detail:
Created Date:  2008-02-25     Completed Date:  2008-03-27     Revised Date:  2008-03-28    
Medline Journal Info:
Nlm Unique ID:  7708048     Medline TA:  Biom J     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  97-109     Citation Subset:  IM    
Copyright Information:
(c) 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Affiliation:
Department of Statistics, University of Toronto, 100 St. George Street, Toronto, Ontario, M5S 3G3, Canada.
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MeSH Terms
Descriptor/Qualifier:
Animal Migration
Animals
Case-Control Studies
Computer Simulation
Data Interpretation, Statistical*
Ecosystem
Female
Logistic Models*
Longitudinal Studies*
Saskatchewan
Comments/Corrections
Erratum In:
Biom J. 2008 Feb;50(1):109

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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