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An introduction to joint models-applications in nephrology.
Nicholas C Chesnaye1, Giovanni Tripepi2, Friedo W Dekker3
1Department of Medical Informatics, ERA-EDTA Registry, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
Clinical Kidney Journal
|April 17, 2020
Summary
This article introduces joint models (JM) for analyzing repeated nephrology data with clinical events. Joint models simultaneously analyze longitudinal measurements and survival data, offering new insights into patient outcomes.
Area of Science:
- Nephrology
- Biostatistics
- Longitudinal Data Analysis
Background:
- Nephrology involves collecting extensive longitudinal data from patients.
- Clinical events are frequently recorded alongside these measurements.
- Simultaneous analysis of these data types is crucial for comprehensive understanding.
Purpose of the Study:
- To introduce the joint modeling (JM) framework for nephrology research.
- To demonstrate the application of JM using clinical examples.
- To provide an overview of the foundational techniques within JM.
Main Methods:
- The joint model (JM) framework is presented.
- JM integrates survival analysis and linear mixed models.
- Clinical nephrology examples illustrate the methodology.
Main Results:
- Joint models enable simultaneous analysis of longitudinal and event data.
- This approach enhances the understanding of patient trajectories and clinical events.
- The article revisits classical survival analysis and linear mixed models as components of JM.
Conclusions:
- Joint modeling offers a powerful framework for nephrology research.
- Simultaneous analysis of longitudinal and event data improves clinical insights.
- JM provides a unified approach to complex patient data in nephrology.
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