Bayesian latent variable models for median regression on multiple outcomes.
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.
Dunson
David B
DB
Biostatistics Branch, MD A3-03, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, North Carolina 27709, USA. dunson1@niehs.nih.gov
Watson
M
M
Taylor
Jack A
JA
D000465:Algorithms; D001499:Bayes Theorem; D020552:Comet Assay / Q000379:methods; D053938:DNA Fragmentation / Q000187:drug effects; D006861:Hydrogen Peroxide / Q000494:pharmacology; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D009152:Mutagenicity Tests / Q000379:methods; D016011:Normal Distribution; D012044:Regression Analysis
D006861:Hydrogen Peroxide