Skip to main content
Article thumbnail
Location of Repository

Joint modeling of longitudinal and time-to-event data: an overview

By Anastasios A. Tsiatis and Marie Davidian

Abstract

A common objective in longitudinal studies is to characterize the relationship between a longitudinal response process and a time-to-event. Considerable recent interest has focused on so-called joint models, where models for the event time distribution and longitudinal data are taken to depend on a common set of latent random effects. In the literature, precise statement of the underlying assumptions typically made for these models has been rare. We review the rationale for and development of joint models, offer insight into the structure of the likelihood for model parameters that clarifies the nature of common assumptions, and describe and contrast some of our recent proposals for implementation and inference

Topics: Key words and phrases, Conditional score, Likelihood, Random effects, Seminonparametric. ∗ Corresponding author’s phone/fax, 1 919-515-1928/+1 919-515-7591 Short title, Joint Modeling Overview
Year: 2004
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.6833
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www4.stat.ncsu.edu/~dav... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.