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A practical guide to the appropriate analysis of eGFR data over time: A simulation study.

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Analyzing estimated glomerular filtration rate (eGFR) changes in clinical trials requires careful statistical methods. A proposed two-slope model effectively analyzes both short-term and long-term effects without increasing Type I error rates.

Keywords:
eGFRmodel misspecificationrandom coefficients modelrate of change analysistwo‐slope model

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Area of Science:

  • Nephrology
  • Clinical Trial Statistics
  • Pharmacometrics

Background:

  • Analyzing longitudinal estimated glomerular filtration rate (eGFR) data in clinical trials is crucial for evaluating treatments in conditions like chronic kidney disease (CKD) and immunoglobulin A nephropathy (IgAN).
  • Non-linear changes in eGFR, potentially due to short-term hemodynamic effects of interventions, pose analytical challenges.
  • Regulatory concerns exist regarding Type I error inflation with standard single-slope analysis models for eGFR rate of change.

Purpose of the Study:

  • To provide practical guidance and statistical methodology for analyzing eGFR rate of change in randomized clinical trials.
  • To propose and evaluate a two-slope statistical model for eGFR data that can simultaneously assess short-term and long-term effects.
  • To offer insights into trial design considerations for eGFR endpoints.

Main Methods:

  • A two-slope statistical model was developed for analyzing eGFR data over time.
  • A simulation study was conducted comparing the two-slope model against single-slope random coefficients models and non-slope based analyses (change from baseline, time-normalized area under the curve).
  • Simulations covered various null and alternative hypotheses to assess model performance.

Main Results:

  • Contrary to prior concerns, simulations demonstrated no Type I error inflation with single or two-slope random coefficient models, even when misspecified.
  • Model misspecification was found to impact statistical power rather than Type I error control.
  • The two-slope model provides a robust approach for analyzing complex eGFR trajectories.

Conclusions:

  • The proposed two-slope model is a reliable statistical tool for analyzing eGFR rate of change in clinical trials, accommodating non-linear patterns.
  • Concerns about Type I error inflation with slope-based models for eGFR are not supported by simulation data.
  • Statistical power, not Type I error control, should be the focus when considering model misspecification in eGFR analyses.