CraiuDuchesneFortin2008

Reference

Craiu, R.V., Duchesne, T., Fortin, D. (2008) Inference Methods for the Conditional Logistic Regression Model with Longitudinal Data. Biometrical Journal, 50(1):97-109.

Abstract

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)

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@ARTICLE { CraiuDuchesneFortin2008,
    AUTHOR = { Craiu, R.V. and Duchesne, T. and Fortin, D. },
    TITLE = { Inference Methods for the Conditional Logistic Regression Model with Longitudinal Data },
    JOURNAL = { Biometrical Journal },
    YEAR = { 2008 },
    VOLUME = { 50 },
    PAGES = { 97-109 },
    NUMBER = { 1 },
    NOTE = { 10.1002/bimj.200610379 },
    ABSTRACT = { 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) },
    OWNER = { brugerolles },
    TIMESTAMP = { 2008.02.18 },
}

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