Latent class models and their application to missing-data patterns in longitudinal studies.
Latent class models have been developed as a flexible way of modeling the correlation of multivariate data, as a method for discovering subpopulations with similar response profiles and as a dimension reduction tool. In this manuscript, we provide a review of some of this literature and describe specific developments in several statistical and substantive areas. We then describe latent class models that could be used for characterizing missing-data patterns in longitudinal studies with regularly spaced observation times, where there is a large amount of intermittent missing data. We illustrate by analyzing data from a longitudinal study of depression, where there were 379 unique missing-data patterns.
Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA. jason_roy@urmc.rochester.edu
Jason
J
Roy
D003863:Depression; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D012044:Regression Analysis