Utility and Validity of Estimated GFR–Based Surrogate Time-to-Event End Points in CKD: A Simulation Study-Reference-Cited by-同舟云学术

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Utility and Validity of Estimated GFR–Based Surrogate Time-to-Event End Points in CKD: A Simulation Study

Published:2014-12 Issue:6 Volume:64 Page:867-879
ISSN:0272-6386
Container-title:American Journal of Kidney Diseases
language:en
Short-container-title:American Journal of Kidney Diseases

Author:

Greene Tom,Teng Chia-Chen,Inker Lesley A.,Redd Andrew,Ying Jian,Woodward Mark,Coresh Josef,Levey Andrew S.

Funder

University of Utah Study Design and Biostatistics Center

National Center for Research Resources and the National Center for Advancing Translational Sciences

National Institutes of Health

Publisher

Elsevier BV

Subject

Nephrology

Reference22 articles.

1. GFR decline as an end point for clinical trials in CKD: a scientific workshop sponsored by the National Kidney Foundation and the US Food and Drug Administration;Levey;Am J Kidney Dis,2014

2. Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality;Coresh;JAMA,2014

3. GFR decline and subsequent risk of established kidney outcomes: a meta-analysis of 37 randomized controlled trials;Lambers Heerspink;Am J Kidney Dis,2014

4. Surrogate end points in clinical trials: are we being misled?;Fleming;Ann Intern Med,1996

5. Related causal frameworks for surrogate outcomes;Joffe;Biometrics,2009

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1. A meta-analysis of randomized controlled clinical trials for implications of acute treatment effects on glomerular filtration rate for long-term kidney protection;Kidney International;2024-06

2. Estimated Glomerular Filtration Rate Slope as an Endpoint in Cardiovascular Trials;Current Heart Failure Reports;2024-05-25

3. Endpunkte zu mikrovaskulären Folgekomplikationen: klinische Endpunkte, patientenberichtete Endpunkte und Surrogatendpunkte, Methodik von Surrogatvalidierungsstudien;Diabetologie und Stoffwechsel;2023-10

4. CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model;KIDNEY MED;2023

5. CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model;Kidney Medicine;2023-09

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