Title: Variable selection and allocation in joint models for longitudinal and time-to-event data via boosting
Authors: Andreas Mayr - University of Bonn (Germany)
Colin Griesbach - FAU Erlangen-Nuernberg (Germany)
Elisabeth Waldmann - Friedrich-Alexander-Universitaet Erlangen-Nuernberg (Germany) [presenting]
Abstract: 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 a data structure common 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. A boosting algorithm rendered possible the selection of covariates even in high-dimensional settings. This algorithm is extended to the automated variable allocation. This approach is necessary when there is no prior knowledge available on the question which dependent variable the individual covariates have influence on.