  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. By making use of 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 the event process allowing new
insights into disease progression. The model is estimated within a Bayesian
framework and implemented in the R-package bamlss.
