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Joint modelling of mixed outcome types using latent variables
1Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. chuck@biostat.ucsf.edu
Abstract:
After a brief review of the use of latent variables to accommodate the correlation among multiple outcomes of mixed types, through theoretical and numerical calculation, the consequences of such a construction are quantified. The effects of including latent variables on marginal inference in these models are contrasted with the situation for jointly normal outcomes. A simulation study illustrates the efficiency and reduction in bias gains possible in using joint models, and analysis of an example from the field of osteoarthritis illustrates potential practical differences.
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