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Köhler, M. ; Umlauf, N.* ; Beyerlein, A. ; Winkler, C. ; Ziegler, A.-G. ; Greven, S.*

Flexible Bayesian additive joint models with an application to type 1 diabetes research.

Biom. J. 59, 1144-1165 (2017)
Postprint Forschungsdaten DOI PMC
Open Access Green
The joint modeling of longitudinal and time-to-event data is an important tool of growing popularity to gain insights into the association between a biomarker and an event process. We develop a general framework of flexible additive joint models that allows the specification of a variety of effects, such as smooth nonlinear, time-varying and random effects, in the longitudinal and survival parts of the models. Our extensions are motivated by the investigation of the relationship between fluctuating disease-specific markers, in this case autoantibodies, and the progression to the autoimmune disease type 1 diabetes. Using Bayesian P-splines, we are in particular able to capture highly nonlinear subject-specific marker trajectories as well as a time-varying association between the marker and event process allowing new insights into disease progression. The model is estimated within a Bayesian framework and implemented in the R-package bamlss.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Anisotropic Smoothing ; Biomarkers ; Longitudinal Data ; P-splines ; Time-to-event Data; To-event Data; Survival-data; Islet Autoantibodies; Smoothing Parameter; Time Data; Regression; Appearance; Selection; Risk; Autoimmunity
ISSN (print) / ISBN 0323-3847
e-ISSN 1521-4036
Zeitschrift Biometrical Journal
Quellenangaben Band: 59, Heft: 6, Seiten: 1144-1165 Artikelnummer: , Supplement: ,
Verlag Wiley
Verlagsort Weinheim
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Diabetes Research Type 1 (IDF)