Premium
Joint modeling of survival and longitudinal non‐survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group
Author(s) -
Lawrence Gould A.,
Boye Mark Ernest,
Crowther Michael J.,
Ibrahim Joseph G.,
Quartey George,
Micallef Sandrine,
Bois Frederic Y.
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6141
Subject(s) - bayesian probability , computer science , joint (building) , longitudinal data , survival analysis , clinical trial , psychological intervention , proportional hazards model , data mining , medicine , statistics , artificial intelligence , mathematics , engineering , architectural engineering , pathology , psychiatry
Explicitly modeling underlying relationships between a survival endpoint and processes that generate longitudinal measured or reported outcomes potentially could improve the efficiency of clinical trials and provide greater insight into the various dimensions of the clinical effect of interventions included in the trials. Various strategies have been proposed for using longitudinal findings to elucidate intervention effects on clinical outcomes such as survival. The application of specifically Bayesian approaches for constructing models that address longitudinal and survival outcomes explicitly has been recently addressed in the literature. We review currently available methods for carrying out joint analyses, including issues of implementation and interpretation, identify software tools that can be used to carry out the necessary calculations, and review applications of the methodology. Copyright © 2014 John Wiley & Sons, Ltd.