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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

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Clinical and Experimental Nephrology
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Chronic kidney diseaseLinear mixed-effects modelReal-world dataeGFR slope

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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.