Background:Most patients with congenital heart disease survive into adulthood; however, residual abnormalities remain and management of the patients is life-long and personalized. Patients with surgical repair of transposition of the great arteries, for example, face the risk to develop neoaortic valve regurgitation. Cardiologists update the prognosis of the patient intuitively with updated information of the cardiovascular status of the patient, for instance from echocardiographic imaging.Methods:Usually a time-dependent version of the Cox model is used to analyze repeated measurements with a time-to-event outcome. New statistical methods have been developed with multiple advantages, of which the most prominent one being the joint model for longitudinal and time-to-event outcome. In this tutorial, the joint modeling framework is introduced and applied to patients with transposition of the great arteries after surgery with a long-term follow-up, where repeated echocardiographic values of the neoaortic root are evaluated against the risk of neoaortic valve regurgitation.Results:The data are analyzed with the time-dependent Cox model as benchmark method, and the results are compared with a joint model, leading to different conclusions. The flexibility of the joint model is shown by adding the growth rate of the neoaortic root to the model and adding repeated values of body surface area to obtain a multimarker model. Lastly, it is demonstrated how the joint model can be used to obtain personalized dynamic predictions of the event.Conclusions:The joint model for longitudinal and time-to-event data is an attractive method to analyze data in follow-up studies with repeated measurements. Benefits of the method include using the estimated natural trajectory of the longitudinal outcome, great flexibility through multiple extensions, and dynamic individualized predictions.

中文翻译：

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纵向标志物和时间到事件结果的联合建模：大动脉转位手术修复后患者的应用和教程

背景：大多数先天性心脏病患者都能活到成年；然而，残留的异常仍然存在，患者的管理是终生的和个性化的。例如，接受大动脉转位手术修复的患者面临发生新主动脉瓣关闭不全的风险。心脏病专家使用患者心血管状态的更新信息直观地更新患者的预后，例如来自超声心动图成像。方法：通常使用 Cox 模型的时间相关版本来分析具有时间到事件的重复测量结果。已经开发出具有多种优势的新统计方法，其中最突出的是纵向和时间到事件结果的联合模型。在本教程中，联合建模框架被引入并应用于长期随访的大动脉转位术后患者，评估新主动脉根部重复超声心动图值与新主动脉瓣关闭不全的风险。结果：数据为以瞬态 Cox 模型为基准方法进行分析，并将结果与​​联合模型进行比较，得出不同的结论。联合模型的灵活性通过将新主动脉根部的生长速率加入模型并加入体表面积的重复值以获得多标记模型来显示。最后，演示了如何使用联合模型来获得事件的个性化动态预测。 结论：纵向和时间到事件数据的联合模型是在重复测量的后续研究中分析数据的一种有吸引力的方法。该方法的好处包括使用纵向结果的估计自然轨迹、通过多次扩展的极大灵活性以及动态个性化预测。