  Joint Models for longitudinal and time-to-event data have gained a lot of
attention in the last few years as they are a helpful technique to approach
common a data structure in clinical studies where longitudinal outcomes are
recorded alongside event times. Those two processes are often linked and the
two outcomes should thus be modeled jointly in order to prevent the potential
bias introduced by independent modelling. Commonly, joint models are estimated
in likelihood based expectation maximization or Bayesian approaches using
frameworks where variable selection is problematic and which do not immediately
work for high-dimensional data. In this paper, we propose a boosting algorithm
tackling these challenges by being able to simultaneously estimate predictors
for joint models and automatically select the most influential variables even
in high-dimensional data situations. We analyse the performance of the new
algorithm in a simulation study and apply it to the Danish cystic fibrosis
registry which collects longitudinal lung function data on patients with cystic
fibrosis together with data regarding the onset of pulmonary infections. This
is the first approach to combine state-of-the art algorithms from the field of
machine-learning with the model class of joint models, providing a fully
data-driven mechanism to select variables and predictor effects in a unified
framework of boosting joint models.
