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

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 common a data structure 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. 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 analyse the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry which 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.

Exploring causality mechanism in the joint analysis of longitudinal and survival data

“…Consequently, CD4 is not a mediator between treatment and survival. Furthermore, the second condition of the definition of surrogate marker 15 ( Y must be affected by treatment A ) does not hold. Therefore, CD4 is not a surrogate marker for survival in this trial.…”

“…Such a latent variable could be continuous, eg, random effects, [5][6][7][8][9][10][11] or discrete, eg, latent class, 12,13 or both. 14 Recently, joint models have also been incorporated in the machine learning framework through statistical boosting 15 and variable selection by adaptive LASSO. 16 In this paper, we will focus on shared random-effects models, though the extension to shared latent class models is straightforward in principle.…”

Flexible Bayesian additive joint models with an application to type 1 diabetes research

“…So far model selection is conducted via DIC. We note that more advanced model selection techniques such as Bayesian Lasso selection (Tang, Zhao, & Tang, 2017) or boosting (Waldmann et al, 2017) have been developed. Including these techniques into the presented framework are topics for future work.…”

An Update on Statistical Boosting in Biomedicine

“…An accompanying article [ 11 ] highlighted the multiple extension of the basic algorithms towards (i) enhanced variable selection properties, (ii) new types of predictor effects, and (iii) new regression settings. Substantial methodological developments on statistical boosting algorithms throughout the last few years (e.g., stability selection [ 12 ]) and a growing community have opened the door to new model classes and frameworks (e.g., joint models [ 13 ] and functional data [ 14 ]), asking for an up-to-date review on the available extensions.…”