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Flexible Bayesian additive joint models with an application to type 1 diabetes research
Author(s) -
Köhler Meike,
Umlauf Nikolaus,
Beyerlein Andreas,
Winkler Christiane,
Ziegler AnetteGabriele,
Greven Sonja
Publication year - 2017
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201600224
Subject(s) - bayesian probability , event (particle physics) , computer science , random effects model , event data , econometrics , machine learning , covariate , mathematics , artificial intelligence , medicine , meta analysis , physics , quantum mechanics
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 .