  In various data situations joint models are an efficient tool to analyze
relationships between time dependent covariates and event times or to correct
for event-dependent dropout occurring in regression analysis. Joint modeling
connects a longitudinal and a survival submodel within a single joint
likelihood which then can be maximized by standard optimization methods. Main
burdens of these conventional methods are that the computational effort
increases rapidly in higher dimensions and they do not offer special tools for
proper variable selection. Gradient boosting techniques are well known among
statisticians for addressing exactly these problems, hence an initial boosting
algorithm to fit a basic joint model based on functional gradient descent
methods has been proposed. Aim of this work is to extend this algorithm in
order to fit a model incorporating baseline covariates affecting solely the
survival part of the model. The extended algorithm is evaluated based on low
and high dimensional simulation runs as well as a data set on AIDS patients,
where the longitudinal submodel models the underlying profile of the CD4 cell
count which then gets included alongside several baseline covariates in the
survival submodel.
