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Review no. 2: a beginner's guide for calculating eGFR slope using linear mixed-effects model in R-step-by-step
Megumi Oshima1, Masahiko Gosho2, Masao Iwagami3,4
1Department of Nephrology and Rheumatology, Kanazawa University, Kanazawa, Ishikawa, Japan.
Clinical and Experimental Nephrology
|February 26, 2026
Summary
Linear mixed-effects models offer a statistically superior method for calculating the estimated glomerular filtration rate (eGFR) slope, crucial for tracking chronic kidney disease progression in clinical studies.
Area of Science:
- Nephrology
- Biostatistics
- Clinical Trials
Background:
- Estimated glomerular filtration rate (eGFR) slope is vital for assessing chronic kidney disease (CKD) progression.
- Observational studies increasingly use eGFR slope as an outcome or exposure.
- Linear mixed-effects models are recommended over individual linear regression for eGFR slope calculation due to statistical efficiency.
Purpose of the Study:
- To demonstrate the practical application of linear mixed-effects models for calculating individual eGFR slopes.
- To illustrate how to compare mean eGFR slopes between different groups using these models.
- To provide guidance on using R programming for these analyses.
Main Methods:
- Utilized linear mixed-effects models to analyze longitudinal eGFR data.
- Incorporated fixed effects for population-level trends and random effects for subject-specific variations.
- Employed R programming for practical implementation of eGFR slope calculations.
Main Results:
- Successfully calculated individual eGFR slopes using linear mixed-effects models.
- Demonstrated the comparison of mean eGFR slopes across different groups.
- Highlighted the efficiency and flexibility of these models in R.
Conclusions:
- Linear mixed-effects models provide a robust framework for estimating eGFR slopes in observational studies.
- These models facilitate accurate assessment of kidney function decline and group differences.
- R programming enables accessible implementation for researchers in nephrology and related fields.
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