Factor analytic models of clustered multivariate data with informative censoring.

Published

Journal Article

This article describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censoring process, and we account for dependency between these latent variables through a hierarchical model. A linear model is used to relate covariates and latent variables to the primary outcomes for each subunit. A generalized linear model accounts for covariate and latent variable effects on the probability of censoring for subunits within each cluster. The model accounts for correlation within clusters and within subunits through a flexible factor analytic framework that allows multiple latent variables and covariate effects on the latent variables. The structure of the model facilitates implementation of Markov chain Monte Carlo methods for posterior estimation. Data from a spermatotoxicity study are analyzed to illustrate the proposed approach.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB; Perreault, SD

Published Date

  • March 2001

Published In

Volume / Issue

  • 57 / 1

Start / End Page

  • 302 - 308

PubMed ID

  • 11252614

Pubmed Central ID

  • 11252614

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/j.0006-341x.2001.00302.x

Language

  • eng