Latent variable models for the analysis of medical data with repeated measures of binary variables.
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.
Blackwood
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Department of Biostatistics, University of Alabama, Birmingham 35294.
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