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The joint modeling of longitudinal and time-to-event data is an importanttool of growing popularity to gain insights into the association between abiomarker and an event process. We develop a general framework of flexibleadditive joint models that allows the specification of a variety of effects,such as smooth nonlinear, time-varying and random effects, in the longitudinaland survival parts of the models. Our extensions are motivated by theinvestigation of the relationship between fluctuating disease-specific markers,in this case autoantibodies, and the progression to the autoimmune disease type1 diabetes. By making use of Bayesian P-splines we are in particular able tocapture highly nonlinear subject-specific marker trajectories as well as atime-varying association between the marker and the event process allowing newinsights into disease progression. The model is estimated within a Bayesianframework and implemented in the R-package bamlss.

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