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arXiv 2102.01466stat.MLcs.LG
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基于大规模纵向生物标志物历史的个体动态临床终点预测：一种界标方法

Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach

INSERM（法国国家健康与医学研究院）；University of Bordeaux（波尔多大学）；INRIA Bordeaux Sud-Ouest（法国国家信息与自动化研究所波尔多西南分部）

Anthony Devaux, Robin Genuer, Karine Pérès, Cécile Proust-Lima

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AI总结：

本研究提出一种结合界标法与机器学习生存分析的方法，利用大规模纵向生物标志物历史进行个体动态临床终点预测，并通过模拟和实际数据验证其优于标准生存模型。

摘要：

在整个患者随访过程中收集的个体数据对于评估临床事件风险以及最终调整治疗策略至关重要。联合模型和界标模型已被提出用于根据重复测量的一到两个标志物计算个体动态预测。然而，当完整的患者病史可能包含更多重复标志物时，这些模型很难扩展。因此，我们的目标是提出一种解决方案，用于健康事件的动态预测，该方案可以利用可能大量标志物的重复测量。我们将扩展到内源性标志物历史的界标方法与适用于生存数据的机器学习方法相结合。每个标志物轨迹使用截至界标时间收集的信息进行建模，并推导出最能捕捉个体轨迹的汇总变量。这些汇总变量和额外的协变量随后被纳入不同的预测方法中。为了处理可能高维的历史数据，我们依赖于适用于生存数据的机器学习方法，即正则化回归和随机生存森林，从界标时间预测事件，并展示了如何将它们组合成一个超级学习器。然后，通过使用适用于删失数据的Brier分数和受试者工作特征曲线下面积的估计量进行交叉验证来评估性能。我们在模拟研究中证明了机器学习生存方法相对于标准生存模型的优势，特别是在预测变量与事件之间存在众多和/或非线性关系的情况下。随后，我们将该方法应用于两个预测场景：临床场景中预测原发性胆汁性胆管炎患者的死亡，以及公共卫生场景中预测不同年龄的普通老年人群的死亡。我们的方法在R中实现，能够利用整个纵向患者病史预测事件，即使重复标志物的数量很大。虽然我们使用混合模型处理重复标志物，并使用适用于单一右删失时间-事件的方法，但我们的方法可以与任何其他合适的标志物建模技术一起使用，并且可以轻松扩展到竞争风险设置。

英文摘要：

The individual data collected throughout patient follow-up constitute crucial information for assessing the risk of a clinical event, and eventually for adapting a therapeutic strategy. Joint models and landmark models have been proposed to compute individual dynamic predictions from repeated measures to one or two markers. However, they hardly extend to the case where the complete patient history includes much more repeated markers possibly. Our objective was thus to propose a solution for the dynamic prediction of a health event that may exploit repeated measures of a possibly large number of markers. We combined a landmark approach extended to endogenous markers history with machine learning methods adapted to survival data. Each marker trajectory is modeled using the information collected up to landmark time, and summary variables that best capture the individual trajectories are derived. These summaries and additional covariates are then included in different prediction methods. To handle a possibly large dimensional history, we rely on machine learning methods adapted to survival data, namely regularized regressions and random survival forests, to predict the event from the landmark time, and we show how they can be combined into a superlearner. Then, the performances are evaluated by cross-validation using estimators of Brier Score and the area under the Receiver Operating Characteristic curve adapted to censored data. We demonstrate in a simulation study the benefits of machine learning survival methods over standard survival models, especially in the case of numerous and/or nonlinear relationships between the predictors and the event. We then applied the methodology in two prediction contexts: a clinical context with the prediction of death for patients with primary biliary cholangitis, and a public health context with the prediction of death in the general elderly population at different ages. Our methodology, implemented in R, enables the prediction of an event using the entire longitudinal patient history, even when the number of repeated markers is large. Although introduced with mixed models for the repeated markers and methods for a single right censored time-to-event, our method can be used with any other appropriate modeling technique for the markers and can be easily extended to competing risks setting.

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