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.

翻译：纵向数据与时间事件数据的联合模型常被用于精准医学的众多应用中，以计算动态个体化预测。影响这些预测精度的两个联合模型组成部分是纵向轨迹的形状，以及将纵向结果历史与事件风险联系起来的函数形式。为所有受试者和随访时间找到能产生准确预测的单一良好设定模型可能具有挑战性，尤其是在考虑多个纵向结局时。在本研究中，我们利用超级学习的概念，避免选择单一模型。具体而言，我们基于不同设定的联合模型库计算动态预测的加权组合。权重通过V折交叉验证选择，以优化预测准确性指标。我们采用的预测准确性指标包括期望二次预测误差和期望预测交叉熵。在一项模拟研究中，我们发现超级学习方法产生的结果与Oracle模型（即测试数据集中表现最佳的模型）非常相似。所有提出的方法均已实现于免费提供的R包JMbayes2中。