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：纵向与时间-事件数据的联合模型常被用于计算精准医学诸多应用中的动态个体化预测。影响这些预测准确性的两个联合模型组件是纵向轨迹的形态以及将纵向结局历史与事件风险相连接的函数形式。要找到能够对所有受试者和随访时间均产生准确预测的单一明确指定模型可能颇具挑战性，尤其是在考虑多个纵向结局时。本研究采用超学习概念，避免选择单一模型。具体而言，我们通过指定来自不同规格联合模型库的加权组合来整合动态预测。权重通过V折交叉验证优化预测准确性指标进行选择。我们采用期望二次预测误差和期望预测交叉熵作为预测准确性度量指标。在模拟研究中发现，超学习方法产生的结果与在测试数据集中表现最优的"神谕模型"（Oracle model）高度相似。所有提出的方法均已在免费提供的R包JMbayes2中实现。