title: Log-linear models for the analysis of matched cohort studies.
abstract: The application of conditional logistic regression to the analysis of matched case-control studies has now become quite customary. In addition, it is well known that software designed to fit linear logistic and log-linear models can be used in these analyses. The application of conditional logistic regression to cohort designs is described, and an approach is developed that adapts the linear logistic and log-linear models for the analysis of prospectively collected data. Specific situations discussed include matched pairs, 2:1 matching, and studies in which some subjects are pair matched and others matched 2:1. The methods are illustrated with numeric examples.
authors.affiliation: Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06510.
authors.forename: T R
authors.initials: TR
authors.lastname: Holford
authors.forename: M B
authors.initials: MB
authors.lastname: Bracken
authors.forename: B
authors.initials: B
authors.lastname: Eskenazi
mesh_terms: D016022:Case-Control Studies; D015331:Cohort Studies; D004781:Environmental Exposure; D005260:Female; D005865:Gestational Age; D006801:Humans; D015233:Models, Statistical; D011225:Pre-Eclampsia / Q000139:chemically induced; D011247:Pregnancy