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

Waldmann E, Taylor-Robinson D, Klein N, Kneib T, Pressler T, Schmid M, Mayr A (2017)

Publication Type: Journal article

Publication year: 2017

Journal

Biometrical Journal Wiley-VCH Verlag

Book Volume: 59

Pages Range: 1104-1121

Journal Issue: 6

DOI: 10.1002/bimj.201600158

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.

Authors with CRIS profile

Elisabeth Bergherr Lehrstuhl für Biometrie und Epidemiologie Andreas Mayr Lehrstuhl für Biometrie und Epidemiologie

Additional Organisation(s)

Interdisziplinäres Zentrum für Klinische Forschung

Involved external institutions

The University of Liverpool GB United Kingdom (GB) Georg-August-Universität Göttingen DE Germany (DE) Rigshospitalet DK Denmark (DK) Rheinische Friedrich-Wilhelms-Universität Bonn DE Germany (DE)

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.

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