Likelihood and Pseudo-likelihood Methods for Semiparametric Joint Models for a Primary Endpoint and Longitudinal Data.

Published

Journal Article

Inference on the association between a primary endpoint and features of longitudinal profiles of a continuous response is of central interest in medical and public health research. Joint models that represent the association through shared dependence of the primary and longitudinal data on random effects are increasingly popular; however, existing inferential methods may be inefficient or sensitive to assumptions on the random effects distribution. We consider a semiparametric joint model that makes only mild assumptions on this distribution and develop likelihood-based inference on the association and distribution, which offers improved performance relative to existing methods that is insensitive to the true random effects distribution. Moreover, the estimated distribution can reveal interesting population features, as we demonstrate for a study of the association between longitudinal hormone levels and bone status in peri-menopausal women.

Full Text

Duke Authors

Cited Authors

  • Li, E; Zhang, D; Davidian, M

Published Date

  • August 2007

Published In

Volume / Issue

  • 51 / 12

Start / End Page

  • 5776 - 5790

PubMed ID

  • 18704154

Pubmed Central ID

  • 18704154

Electronic International Standard Serial Number (EISSN)

  • 1872-7352

International Standard Serial Number (ISSN)

  • 0167-9473

Digital Object Identifier (DOI)

  • 10.1016/j.csda.2006.10.008

Language

  • eng