Department of Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands; School of Medicine, University of Belgrade, Belgrade, Serbia.
Department of Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands.
Kidney Int. 2018 Jul;94(1):214-217. doi: 10.1016/j.kint.2018.04.007. Epub 2018 May 24.
In nephrology, repeated measures are frequently available (glomerular filtration rate or proteinuria) and linked to adverse outcomes. However, several features of these longitudinal data should be considered before making such inferences. These considerations are discussed, and we describe how joint modeling of repeatedly measured and time-to-event data may help to assess disease dynamics and to derive personalized prognosis. Joint modeling combines linear mixed-effects models and Cox regression model to relate patient-specific trajectory to their prognosis. We describe several aspects of the relationship between time-varying markers and the endpoint of interest that are assessed with real examples to illustrate the aforementioned aspects of the longitudinal data provided. Thus, joint models are valuable statistical tools for study purposes but also may help health care providers in making well-informed dynamic medical decisions.
在肾脏病学中，经常可以获得重复测量数据（肾小球滤过率或蛋白尿），并且这些数据与不良结局相关联。然而，在进行此类推断之前，应考虑这些纵向数据的几个特征。本文讨论了这些考虑因素，并描述了如何联合使用重复测量数据和事件时间数据的模型来评估疾病动态并得出个性化预后。联合模型将线性混合效应模型和 Cox 回归模型相结合，以将患者特定的轨迹与他们的预后相关联。我们描述了时间变化标记物与感兴趣终点之间关系的几个方面，并用实际例子来说明所提供的纵向数据的上述方面。因此，联合模型是用于研究目的的有价值的统计工具，也可以帮助医疗保健提供者做出明智的动态医疗决策。
