An introduction to joint models—applications in nephrology
Clinical Kidney Journal, vol. 13, pp. 143–149
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.
Authors 6
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Nicholas C Chesnaye corresponding
University of Amsterdam · Amsterdam University Medical Centers · Amsterdam Public Health
Affiliation as printed
Department of Medical Informatics, ERA-EDTA Registry, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
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National Research Council · Istituto di Fisiologia Clinica
Affiliation as printed
Research Unit of Epidemiology and Physiopathology of Renal Diseases and Hypertension, CNR-IFC of Reggio Calabria, Reggio Calabria, Italy
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Leiden University Medical Center
Affiliation as printed
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Affiliation as printed
CNR-IFC, Clinical Epidemiology of Renal Diseases and Hypertension, Reggio Calabria, Italy
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University of Amsterdam · Amsterdam University Medical Centers
Affiliation as printed
Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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University of Amsterdam · Amsterdam University Medical Centers · Amsterdam Public Health
Affiliation as printed
Department of Medical Informatics, ERA-EDTA Registry, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
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