title: Joint modelling of mixed outcome types using latent variables.
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.
authors.affiliation: Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. chuck@biostat.ucsf.edu
authors.forename: Charles
authors.initials: C
authors.lastname: McCulloch
mesh_terms: D000465:Algorithms; D006297:Health Services Accessibility; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D020370:Osteoarthritis, Knee / Q000473:pathology; D017063:Outcome Assessment, Health Care; D016012:Poisson Distribution