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Statistical Methods in Medical Research
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0962280206075309v1
16/5/399    most recent
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Bayesian methods for latent trait modelling of longitudinal data

David B. Dunson

Biostatistics Branch, National Institute of Environmental Health Sciences, NC, USA, dunson1{at}niehs.nih.gov

Latent trait models have long been used in the social science literature for studying variables that can only be measured indirectly through multiple items. However, such models are also very useful in accounting for correlation in multivariate and longitudinal data, particularly when outcomes have mixed measurement scales. Bayesian methods implemented with Markov chain Monte Carlo provide a flexible framework for routine fitting of a broad class of latent variable (LV) models, including very general structural equation models. However, in considering LV models, a number of challenging issues arise, including identifiability, confounding between the mean and variance, uncertainty in different aspects of the model, and difficulty in computation. Motivated by the problem of modelling multidimensional longitudinal data, this article reviews the recent literature, provides some recommendations and highlights areas in need of additional research, focusing on methods for model uncertainty.

This version was published on October 1, 2007

Statistical Methods in Medical Research, Vol. 16, No. 5, 399-415 (2007)
DOI: 10.1177/0962280206075309
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