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An introduction to joint models—applications in nephrology
- Source :
- Clinical Kidney Journal
- Publication Year :
- 2020
- Publisher :
- Oxford University Press (OUP), 2020.
-
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.
- Subjects :
- Nephrology
medicine.medical_specialty
Dynamic prediction
030232 urology & nephrology
Machine learning
computer.software_genre
informative censoring
01 natural sciences
Data type
Generalized linear mixed model
010104 statistics & probability
03 medical and health sciences
0302 clinical medicine
Internal medicine
Medicine
In patient
0101 mathematics
Survival analysis
CKJ Reviews
Transplantation
business.industry
joint models
dynamic prediction
methodology
Informative censoring
epidemiology
Artificial intelligence
business
Joint (audio engineering)
computer
Subjects
Details
- ISSN :
- 20488513 and 20488505
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- Clinical Kidney Journal
- Accession number :
- edsair.doi.dedup.....80ac303813d1bec116c43bd3c93e2052
- Full Text :
- https://doi.org/10.1093/ckj/sfaa024