Boosting joint models for longitudinal and time-to-event data

Journal article


Publication Details

Author(s): Waldmann E, Taylor-Robinson D, Klein N, Kneib T, Pressler T, Schmid M, Mayr A
Journal: Biometrical Journal
Publication year: 2017
Volume: 59
Journal issue: 6
Pages range: 1104-1121
ISSN: 0323-3847


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 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 modeling. Commonly, joint models are estimated in likelihood-based expectation maximization or Bayesian approaches using frameworks where variable selection is problematic and that 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 analyze the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry that 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.


FAU Authors / FAU Editors

Mayr, Andreas Dr.
Lehrstuhl für Biometrie und Epidemiologie
Waldmann, Elisabeth Dr.
Lehrstuhl für Biometrie und Epidemiologie


External institutions with authors

Georg-August-Universität Göttingen
Rheinische Friedrich-Wilhelms-Universität Bonn
Rigshospitalet
The University of Liverpool


How to cite

APA:
Waldmann, E., Taylor-Robinson, D., Klein, N., Kneib, T., Pressler, T., Schmid, M., & Mayr, A. (2017). Boosting joint models for longitudinal and time-to-event data. Biometrical Journal, 59(6), 1104-1121. https://dx.doi.org/10.1002/bimj.201600158

MLA:
Waldmann, Elisabeth, et al. "Boosting joint models for longitudinal and time-to-event data." Biometrical Journal 59.6 (2017): 1104-1121.

BibTeX: 

Last updated on 2018-20-10 at 23:08