title: Latent variable models for the analysis of medical data with repeated measures of binary variables.
abstract: Consideration of within-subject dependencies is a key issue in modelling binary repeated measures medical data. Borrowing from recent developments in sociology and psychology, we demonstrate the applicability of a latent variable approach to the analysis of such data. In particular we present the Rasch model as a basic model for representing the relationship of subject and treatment parameters. The latent variable approach is useful in providing a theoretical framework for specifying dependencies exactly and also as a base for considering more complicated relationships between repeated measures variables.
authors.affiliation: Department of Biostatistics, University of Alabama, Birmingham 35294.
authors.forename: L
authors.initials: L
authors.lastname: Blackwood
mesh_terms: D001294:Attitude to Health; D002056:Burns / Q000523:psychology; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans; D015233:Models, Statistical; D012307:Risk Factors; D013997:Time Factors