Bayesian latent variable models for median regression on multiple outcomes.

Published

Journal Article

Often a response of interest cannot be measured directly and it is necessary to rely on multiple surrogates, which can be assumed to be conditionally independent given the latent response and observed covariates. Latent response models typically assume that residual densities are Gaussian. This article proposes a Bayesian median regression modeling approach, which avoids parametric assumptions about residual densities by relying on an approximation based on quantiles. To accommodate within-subject dependency, the quantile response categories of the surrogate outcomes are related to underlying normal variables, which depend on a latent normal response. This underlying Gaussian covariance structure simplifies interpretation and model fitting, without restricting the marginal densities of the surrogate outcomes. A Markov chain Monte Carlo algorithm is proposed for posterior computation, and the methods are applied to single-cell electrophoresis (comet assay) data from a genetic toxicology study.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB; Watson, M; Taylor, JA

Published Date

  • June 2003

Published In

Volume / Issue

  • 59 / 2

Start / End Page

  • 296 - 304

PubMed ID

  • 12926714

Pubmed Central ID

  • 12926714

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/1541-0420.00036

Language

  • eng