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Estimand Framework Development for eGFR Slope Estimation and Comparative Analyses Across Various Estimation Methods.

Tuo Wang1, Yu Du2

  • 1Global Statistical Sciences, Eli Lilly and Company, Indianapolis, Indiana, USA. tuo.wang@lilly.com.

Therapeutic Innovation & Regulatory Science
|February 25, 2026
PubMed
Summary
This summary is machine-generated.

A standardized framework for estimating the estimated glomerular filtration rate (eGFR) slope is proposed to improve chronic kidney disease (CKD) clinical trial results. This approach enhances the reliability and comparability of CKD therapeutic development.

Keywords:
EGFR slopeEstimandChronic kidney diseaseMixed-effects modelsSurrogate endpoint

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Area of Science:

  • Nephrology
  • Clinical Trials
  • Biostatistics

Background:

  • Chronic kidney disease (CKD) poses a global health burden, with progression to end-stage kidney disease (ESKD) leading to increased mortality.
  • Traditional kidney composite endpoints in CKD trials require long follow-up periods.
  • The estimated glomerular filtration rate (eGFR) slope is a valuable surrogate endpoint for kidney function decline, but lacks standardized estimation methods.

Purpose of the Study:

  • To propose a novel estimand framework for eGFR slope-based analyses in CKD randomized controlled trials (RCTs).
  • To enhance clarity in defining estimands and improve the comparability of results across CKD trials.
  • To evaluate the performance of various estimation techniques for eGFR slope under different scenarios.

Main Methods:

  • Development of a tailored estimand framework for eGFR slope in CKD RCTs.
  • Conducting simulation studies to assess estimation techniques.
  • Applying methods to real-world CKD patient data.

Main Results:

  • The proposed framework provides a clear definition for eGFR slope estimands.
  • Evaluation identified suitable estimation approaches, particularly for scenarios with low competing event rates.
  • The study highlights the importance of aligning estimation methods with specific trial objectives.

Conclusions:

  • A standardized estimand framework is crucial for reliable and interpretable eGFR slope analyses in CKD trials.
  • This work advances therapeutic development by improving the consistency of CKD trial outcomes.
  • The findings support better clinical decision-making in managing chronic kidney disease.