An introduction to joint models-applications in nephrology

Nicholas C Chesnaye; Giovanni Tripepi; Friedo W Dekker; Carmine Zoccali; Aeilko H Zwinderman; Kitty J Jager

doi:10.1093/ckj/sfaa024

An introduction to joint models-applications in nephrology

Clin Kidney J. 2020 Apr 8;13(2):143-149. doi: 10.1093/ckj/sfaa024. eCollection 2020 Apr.

Authors

Nicholas C Chesnaye¹, Giovanni Tripepi², Friedo W Dekker³, Carmine Zoccali⁴, Aeilko H Zwinderman⁵, Kitty J Jager¹

Affiliations

¹ Department of Medical Informatics, ERA-EDTA Registry, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
² Research Unit of Epidemiology and Physiopathology of Renal Diseases and Hypertension, CNR-IFC of Reggio Calabria, Reggio Calabria, Italy.
³ Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
⁴ CNR-IFC, Clinical Epidemiology of Renal Diseases and Hypertension, Reggio Calabria, Italy.
⁵ Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.

Abstract

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.

Keywords: dynamic prediction; epidemiology; informative censoring; joint models; methodology.

Publication types

Review