Joint modelling of mixed outcome types using latent variables.
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.
McCulloch
Charles
C
Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. chuck@biostat.ucsf.edu
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