Dynamic latent trait models for multidimensional longitudinal data

• 成果类型: Article
• 署名作者: Dunson, DB
• 署名单位: National Institutes of Health (NIH) - USA; NIH National Institute of Environmental Health Sciences (NIEHS)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214503000000387
• 发表日期: 2003
• 页码: 555-563
• 关键词: GENERALIZED LINEAR-MODELS; variable models; polytomous data; likelihood; outcomes

摘要

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.