Mixed‐effects models for slope‐based endpoints in clinical trials of chronic kidney disease
Open accessAbstract
In March of 2018, the National Kidney Foundation, in collaboration with the US Food and Drug Administration and the European Medicines Agency, sponsored a workshop in which surrogate endpoints other than currently established event‐time endpoints for clinical trials in chronic kidney disease (CKD) were presented and discussed. One such endpoint is a slope‐based parameter describing the rate of decline in the estimated glomerular filtration rate (eGFR) over time. There are a number of challenges that can complicate such slope‐based analyses in CKD trials. These include the possibility of an early but short‐term acute treatment effect on the slope, both within‐subject and between‐subject heteroscedasticity, and informative censoring resulting from patient dropout due to death or onset of end‐stage kidney disease. To address these issues, we first consider a class of mixed‐effects models for eGFR that are linear in the parameters describing the mean eGFR trajectory but which are intrinsically nonlinear when a power‐of‐mean variance structure is used to model within‐subject heteroscedasticity. We then combine the model for eGFR with a model for time to dropout to form a class of shared parameter models which, under the right specification of shared random effects, can minimize bias due to informative censoring. The models and methods of analysis are described and illustrated using data from two CKD studies one of which was one of 56 studies made available to the workshop analytical team. Lastly, methodology and accompanying software for prospectively determining sample size/power estimates are presented.
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To examine potential survivor bias in the analyses of eGFR slopes, eGFR was also modeled jointly with all-cause mortality.
“In an exploratory analysis, to examine the potential survivor bias introduced into the analyses of eGFR slopes, we also modeled eGFR jointly with all-cause mortality.”
From Efficacy of Dapagliflozin on Renal Function and Outcomes in Patients With Heart Failure With Reduced Ejection Fraction: Results of DAPA-HF (2021) · cites this paper as [16]
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- Authors
- Vonesh, E., Tighiouart, H., Ying, J., Heerspink, H. L., Lewis, J., Staplin, N., Inker, L., & Greene, T.
- Affiliations
- Northwestern University (US)
- Tufts Medical Center (US)
- University of Utah (US)
- Department of Clinical Pharmacy and Pharmacology University Medical Center Groningen Groningen The Netherlands
- Vanderbilt University (US)
- University of Oxford (GB)
- Corresponding author
- Vonesh, Edward
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2019-07-23
- Volume
- 38
- Issue
- 22
- Pages
- 4218-4239
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2962892209
- Cited by
- 78
- References
- 30
- Citations by year
- 2026: 16 · 2025: 18 · 2024: 11 · 2023: 14 · 2022: 8 · 2021: 5 · 2020: 4 · 2019: 2
- FWCI
- 3.70
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-05-09)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Statistics in Medicine (publishedVersion, other-oa)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Funders
- National Kidney Foundation
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Keywords
- chronic kidney disease, surrogate endpoints, estimated glomerular filtration rate decline, mixed models, shared parameter models, informative censoring, dropout, heteroscedasticity, end-stage renal disease
- MeSH terms
- Clinical Trials as Topic, Computer Simulation, Humans, Models, Statistical, Treatment Outcome, Endpoint Determination, Renal Insufficiency, Chronic
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