Robust Bayesian inference for multivariate longitudinal data by using normal/independent distributions.

Published

Journal Article

Many randomized clinical trials collect multivariate longitudinal measurements in different scales, for example, binary, ordinal, and continuous. Multilevel item response models are used to evaluate the global treatment effects across multiple outcomes while accounting for all sources of correlation. Continuous measurements are often assumed to be normally distributed. But the model inference is not robust when the normality assumption is violated because of heavy tails and outliers. In this article, we develop a Bayesian method for multilevel item response models replacing the normal distributions with symmetric heavy-tailed normal/independent distributions. The inference is conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in BUGS language. Our proposed method is evaluated by simulation studies and is applied to Earlier versus Later Levodopa Therapy in Parkinson's Disease study, a motivating clinical trial assessing the effect of Levodopa therapy on the Parkinson's disease progression rate.

Full Text

Duke Authors

Cited Authors

  • Luo, S; Ma, J; Kieburtz, KD

Published Date

  • September 30, 2013

Published In

Volume / Issue

  • 32 / 22

Start / End Page

  • 3812 - 3828

PubMed ID

  • 23494809

Pubmed Central ID

  • 23494809

Electronic International Standard Serial Number (EISSN)

  • 1097-0258

Digital Object Identifier (DOI)

  • 10.1002/sim.5778

Language

  • eng

Conference Location

  • England