Dynamic event prediction, using joint modeling of survival time and longitudinal variables, is extremely useful in personalized medicine. However, the estimation of joint models including many longitudinal markers is still a computational challenge because of the high number of random effects and parameters to be estimated. In this paper, we propose a model averaging strategy to combine predictions from several joint models for the event, including one longitudinal marker only or pairwise longitudinal markers. The prediction is computed as the weighted mean of the predictions from the one-marker or two-marker models, with the time-dependent weights estimated by minimizing the time-dependent Brier score. This method enables us to combine a large number of predictions issued from joint models to achieve a reliable and accurate individual prediction. Advantages and limits of the proposed methods are highlighted in a simulation study by comparison with the predictions from well-specified and misspecified all-marker joint models as well as the one-marker and two-marker joint models. Using the PBC2 data set, the method is used to predict the risk of death in patients with primary biliary cirrhosis. The method is also used to analyze a French cohort study called the 3C data. In our study, seventeen longitudinal markers are considered to predict the risk of death.

翻译：利用生存时间与纵向变量的联合建模进行动态事件预测，在个性化医疗中极具价值。然而，包含多个纵向标志物的联合模型因其需要估计大量随机效应和参数，在计算上仍面临挑战。本文提出一种模型平均策略，通过整合多个仅包含单一纵向标志物或成对纵向标志物的联合模型预测结果来实现事件预测。预测值由单标志物或双标志物模型的预测结果加权平均得到，其随时间变化的权重通过最小化时间依赖性Brier评分进行估计。该方法能够整合大量来自联合模型的预测结果，从而获得可靠且准确的个体化预测。通过模拟研究，将所提方法与正确设定及错误设定的全标志物联合模型、单标志物及双标志物联合模型的预测结果进行比较，凸显了该方法的优势与局限。利用PBC2数据集，该方法被用于预测原发性胆汁性肝硬化患者的死亡风险，并应用于法国3C队列研究的数据分析。在本研究中，共采用十七个纵向标志物进行死亡风险预测。