PuSH - Publikationsserver des Helmholtz Zentrums München

Köhler, M. ; Umlauf, N.* ; Greven, S.*

Nonlinear association structures in flexible Bayesian additive joint models.

Stat. Med. 37, 4771-4788 (2018)
Postprint DOI
Open Access Green
Joint models of longitudinal and survival data have become an important tool for modeling associations between longitudinal biomarkers and event processes. The association between marker and log hazard is assumed to be linear in existing shared random effects models, with this assumption usually remaining unchecked. We present an extended framework of flexible additive joint models that allows the estimation of nonlinear covariate specific associations by making use of Bayesian P-splines. Our joint models are estimated in a Bayesian framework using structured additive predictors for all model components, allowing for great flexibility in the specification of smooth nonlinear, time-varying, and random effects terms for longitudinal submodel, survival submodel, and their association. The ability to capture truly linear and nonlinear associations is assessed in simulations and illustrated on the widely studied biomedical data on the rare fatal liver disease primary biliary cirrhosis. All methods are implemented in the R package bamlss to facilitate the application of this flexible joint model in practice.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Joint Model ; Longitudinal Data ; Nonlinear Association ; P-splines ; Time-to-event Data; To-event Data; Survival-data; R Package; Regression; Splines; Predictions
ISSN (print) / ISBN 0277-6715
e-ISSN 1097-0258
Zeitschrift Statistics in Medicine
Quellenangaben Band: 37, Heft: 30, Seiten: 4771-4788 Artikelnummer: , Supplement: ,
Verlag Wiley
Verlagsort 111 River St, Hoboken 07030-5774, Nj Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Diabetes Research Type 1 (IDF)