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Inference Methods for the Conditional Logistic Regression Model with Longitudinal Data
Author(s) -
Craiu Radu V.,
Duchesne Thierry,
Fortin Daniel
Publication year - 2008
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200610379
Subject(s) - logistic regression , statistics , econometrics , inference , longitudinal data , cross sectional regression , computer science , regression analysis , mathematics , artificial intelligence , data mining , polynomial regression
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. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)