Latent-model robustness in joint models for a primary endpoint and a longitudinal process.

Journal Article (Journal Article)

Joint modeling of a primary response and a longitudinal process via shared random effects is widely used in many areas of application. Likelihood-based inference on joint models requires model specification of the random effects. Inappropriate model specification of random effects can compromise inference. We present methods to diagnose random effect model misspecification of the type that leads to biased inference on joint models. The methods are illustrated via application to simulated data, and by application to data from a study of bone mineral density in perimenopausal women and data from an HIV clinical trial.

Full Text

Duke Authors

Cited Authors

  • Huang, X; Stefanski, LA; Davidian, M

Published Date

  • September 2009

Published In

Volume / Issue

  • 65 / 3

Start / End Page

  • 719 - 727

PubMed ID

  • 19173697

Pubmed Central ID

  • PMC2748157

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

Digital Object Identifier (DOI)

  • 10.1111/j.1541-0420.2008.01171.x


  • eng

Conference Location

  • United States