Standard Article

Joint Models

Ecological Statistics

  1. Darby J. S. Thompson

Published Online: 15 JAN 2013

DOI: 10.1002/9780470057339.vnn143

Encyclopedia of Environmetrics

Encyclopedia of Environmetrics

How to Cite

Thompson, D. J. S. 2013. Joint Models. Encyclopedia of Environmetrics. 3.

Author Information

  1. Simon Fraser University, Department of Statistics and Actuarial Science, Burnaby, British Columbia, Canada

Publication History

  1. Published Online: 15 JAN 2013

Abstract

Joint modeling encompasses strategies to simultaneously model several outcomes of interest. There are three principal strategies; classical joint modeling, conditional models, and conditional independence models. Likely the most pervasive area of joint modeling is in the modeling of longitudinal and time-to-event data; in particular, accounting for drop-out in longitudinal data or incorporating error-prone, sporadically measured, longitudinal outcomes in models for event times. Conditional independence is a popular strategy, which assumes the outcomes of interest are noisy, independent measures of some underlying latent process; it is this process that induces their correlation providing a tractable assumption in many practical settings.

Keywords:

  • joint model;
  • conditional model;
  • conditional independence model;
  • hierarchical model;
  • mixed outcomes