Optimizing Dynamic Predictions from Joint Models using Super Learning

TL;DR

This paper introduces a super learning approach to optimize dynamic predictions from joint models of longitudinal and time-to-event data, enhancing accuracy without selecting a single model, and provides an R package implementation.

Contribution

It proposes a novel ensemble method combining multiple joint models to improve predictive accuracy in precision medicine applications.

Findings

Super learning achieves performance close to the Oracle model.

The method improves prediction accuracy across various simulation scenarios.

Implementation is available in the R package JMbayes2.

Abstract

Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two components of joint models that influence the accuracy of these predictions are the shape of the longitudinal trajectories and the functional form linking the longitudinal outcome history to the hazard of the event. Finding a single well-specified model that produces accurate predictions for all subjects and follow-up times can be challenging, especially when considering multiple longitudinal outcomes. In this work, we use the concept of super learning and avoid selecting a single model. In particular, we specify a weighted combination of the dynamic predictions calculated from a library of joint models with different specifications. The weights are selected to optimize a predictive accuracy metric using V-fold cross-validation. We use as predictive accuracy measures the expected quadratic prediction error and the expected predictive cross-entropy. In a simulation study, we found that the super learning approach produces results very similar to the Oracle model, which was the model with the best performance in the test datasets. All proposed methodology is implemented in the freely available R package JMbayes2.