  The joint modeling of multiple longitudinal biomarkers together with a
time-to-event outcome is a challenging modeling task of continued scientific
interest. In particular, the computational complexity of high dimensional
(generalized) mixed effects models often restricts the flexibility of shared
parameter joint models, even when the subject-specific marker trajectories
follow highly nonlinear courses. We propose a parsimonious multivariate
functional principal components representation of the shared random effects.
This allows better scalability, as the dimension of the random effects does not
directly increase with the number of markers, only with the chosen number of
principal component basis functions used in the approximation of the random
effects. The functional principal component representation additionally allows
to estimate highly flexible subject-specific random trajectories without
parametric assumptions. The modeled trajectories can thus be distinctly
different for each biomarker. We build on the framework of flexible Bayesian
additive joint models implemented in the R-package 'bamlss', which also
supports estimation of nonlinear covariate effects via Bayesian P-splines. The
flexible yet parsimonious functional principal components basis used in the
estimation of the joint model is first estimated in a preliminary step. We
validate our approach in a simulation study and illustrate its advantages by
analyzing a study on primary biliary cholangitis.
