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

DOI10.1002/bimj.201600224zbMath1379.62073arXiv1611.01485OpenAlexW2550054051WikidataQ38630657 ScholiaQ38630657MaRDI QIDQ4597903

Andreas Beyerlein, Nikolaus Umlauf, Anette-Gabriele Ziegler, Christiane Winkler, Sonja Greven, Meike Köhler

Publication date: 14 December 2017

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1611.01485

zbMATH Keywords

longitudinal dataP-splinesbiomarkerstime-to-event dataanisotropic smoothing

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (5)

Spatial joint models through Bayesian structured piecewise additive joint modelling for longitudinal and time-to-event data ⋮ Stable non-linear generalized Bayesian joint models for survival-longitudinal data ⋮ BAMLSS: Bayesian Additive Models for Location, Scale, and Shape (and Beyond) ⋮ Automatic search intervals for the smoothing parameter in penalized splines ⋮ bamlss

Uses Software

• R

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