Dynamic Latent Trait Models for Multidimensional Longitudinal Data

Published

Journal Article

This article presents a new approach for analysis of multidimensional longitudinal data, motivated by studies using an item response battery to measure traits of an individual repeatedly over time. A general modeling framework is proposed that allows mixtures of count, categorical, and continuous response variables. Each response is related to age-specific latent traits through a generalized linear model that accommodates item-specific measurement errors. A transition model allows the latent traits at a given age to depend on observed predictors and on previous latent traits for that individual. Following a Bayesian approach to inference, a Markov chain Monte Carlo algorithm is proposed for posterior computation. The methods are applied to data from a neurotoxicity study of the pesticide methoxychlor, and evidence of a dose-dependent increase in motor activity is presented.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB

Published Date

  • September 1, 2003

Published In

Volume / Issue

  • 98 / 463

Start / End Page

  • 555 - 563

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1198/016214503000000387

Citation Source

  • Scopus