Boosting joint models for longitudinal and time-to-event data.
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.
Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Waldstraße 6, 91054, Erlangen, Germany.
Elisabeth
http://orcid.org/0000-0003-3983-9957
E
Waldmann
Department of Public Health and Policy, Farr Institute, University of Liverpool, Liverpool, L69 3GL, United Kingdom.
David
D
Taylor-Robinson
Chairs of Statistics and Econometrics, Georg-August-Universität Göttingen, Humboldtallee 3, 37073, Göttingen, Germany.
Nadja
N
Klein
Chairs of Statistics and Econometrics, Georg-August-Universität Göttingen, Humboldtallee 3, 37073, Göttingen, Germany.
Thomas
T
Kneib
Cystic Fibrosis Center, Rigshospitalet, Copenhagen, Denmark.
Tania
T
Pressler
Department of Medical Biometrics, Informatics and Epidemiology, Rheinische Friedrich-Wilhelms-Universität Bonn, Sigmund-Freud-Straße 25, 53105, Bonn, Germany.
Matthias
M
Schmid
Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Waldstraße 6, 91054, Erlangen, Germany.|Department of Medical Biometrics, Informatics and Epidemiology, Rheinische Friedrich-Wilhelms-Universität Bonn, Sigmund-Freud-Straße 25, 53105, Bonn, Germany.
Andreas
A
Mayr
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D003550:Cystic Fibrosis / Q000453:epidemiology; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D000069550:Machine Learning; D015233:Models, Statistical; D012042:Registries; D013997:Time Factors
Boosting; High-dimensional data; Joint modeling; Longitudinal models; Time-to-event analysis; Variable selection