Chesnaye Nicholas C, Tripepi Giovanni, Dekker Friedo W, Zoccali Carmine, Zwinderman Aeilko H, Jager Kitty J
Department of Medical Informatics, ERA-EDTA Registry, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
Research Unit of Epidemiology and Physiopathology of Renal Diseases and Hypertension, CNR-IFC of Reggio Calabria, Reggio Calabria, Italy.
Clin Kidney J. 2020 Apr 8;13(2):143-149. doi: 10.1093/ckj/sfaa024. eCollection 2020 Apr.
In nephrology, a great deal of information is measured repeatedly in patients over time, often alongside data on events of clinical interest. In this introductory article we discuss how these two types of data can be simultaneously analysed using the joint model (JM) framework, illustrated by clinical examples from nephrology. As classical survival analysis and linear mixed models form the two main components of the JM framework, we will also briefly revisit these techniques.
在肾脏病学中，随着时间推移，会对患者反复测量大量信息，这些信息通常与临床关注事件的数据一起获取。在这篇介绍性文章中，我们将讨论如何使用联合模型（JM）框架同时分析这两类数据，并通过肾脏病学的临床实例进行说明。由于经典生存分析和线性混合模型构成了JM框架的两个主要组成部分，我们还将简要回顾这些技术。
