Joint Models
Book Chapter
Authors:
Thompson, D.J.S.
Secondary:
Encyclopedia of Environmetrics
Pagination:
1405-1408
Location: Chichester, UK
Publisher: John Wiley & Sons, Ltd.
URL:
http://onlinelibrary.wiley.com/doi/10.1002/9780470057339.vnn143/abstract
Keywords:
conditional independence model; conditional model; hierarchical model; joint model; mixed outcomes
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.
