title: Inference methods for the conditional logistic regression model with longitudinal data.
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.
authors.affiliation: Department of Statistics, University of Toronto, 100 St. George Street, Toronto, Ontario, M5S 3G3, Canada.
authors.forename: Radu V
authors.initials: RV
authors.lastname: Craiu
authors.forename: Thierry
authors.initials: T
authors.lastname: Duchesne
authors.forename: Daniel
authors.initials: D
authors.lastname: Fortin
mesh_terms: D025041:Animal Migration; D000818:Animals; D016022:Case-Control Studies; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D017753:Ecosystem; D005260:Female; D016015:Logistic Models; D008137:Longitudinal Studies; D012525:Saskatchewan