Conditional estimation for generalized linear models when covariates are subject-specific parameters in a mixed model for longitudinal measurements.

Journal Article (Journal Article)

The relationship between a primary endpoint and features of longitudinal profiles of a continuous response is often of interest, and a relevant framework is that of a generalized linear model with covariates that are subject-specific random effects in a linear mixed model for the longitudinal measurements. Naive implementation by imputing subject-specific effects from individual regression fits yields biased inference, and several methods for reducing this bias have been proposed. These require a parametric (normality) assumption on the random effects, which may be unrealistic. Adapting a strategy of Stefanski and Carroll (1987, Biometrika74, 703-716), we propose estimators for the generalized linear model parameters that require no assumptions on the random effects and yield consistent inference regardless of the true distribution. The methods are illustrated via simulation and by application to a study of bone mineral density in women transitioning to menopause.

Full Text

Duke Authors

Cited Authors

  • Li, E; Zhang, D; Davidian, M

Published Date

  • March 2004

Published In

Volume / Issue

  • 60 / 1

Start / End Page

  • 1 - 7

PubMed ID

  • 15032767

Pubmed Central ID

  • PMC1628348

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/j.0006-341X.2004.00170.x


  • eng

Conference Location

  • United States