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Latent class models and their application to missing-data patterns in longitudinal studies

Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA, jason_roy{at}urmc.rochester.edu

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.

This version was published on October 1, 2007

Statistical Methods in Medical Research, Vol. 16, No. 5, 441-456 (2007)
DOI: 10.1177/0962280206075311


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