{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T10:04:49Z","timestamp":1789380289634,"version":"build-2803163510"},"reference-count":26,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In observational studies of glomerular disease, baseline estimated glomerular filtration rate (eGFR) at biopsy is commonly defined using the first available measurement within 6 months. However, eGFR may change rapidly around biopsy, and values obtained distant from the biopsy may not reflect true baseline kidney function. When no eGFR is available, baseline eGFR are often treated as missing and imputed, which may introduce bias if the imputed values do not accurately represent baseline kidney function. To date, no studies have evaluated the accuracy of different imputation strategies for baseline eGFR in glomerular disease. Given the availability of repeated eGFR measurements in many glomerular disease cohorts, we hypothesized that incorporating longitudinal eGFR data into a mixed-effects modeling framework would improve the accuracy of imputing missing baseline eGFR. Using a population-based glomerular disease cohort in British Columbia, Canada, we defined true baseline eGFR as the closest value within 15 days of biopsy. We compared the closest eGFR within varying time windows to the true baseline eGFR using mean absolute error. Predicted baseline eGFR was generated using a mixed-effects model incorporating repeated eGFR measurements. We evaluated the accuracy of single and multiple imputation by comparing imputed values to the true baseline eGFR. Among 2874 patients, error increased as the time window for selecting the closest eGFR from the biopsy date widened. In single imputation, predicted baseline eGFR based on the mixed-effects model consistently showed greater accuracy than the closest observed values. In multiple imputation, incorporating predicted baseline eGFR improved both the accuracy and precision of imputed values. In conclusion, using eGFR values distant from the time of biopsy may misclassify baseline kidney function. Incorporating longitudinal eGFR data through mixed-effects modeling improves both single and multiple imputation, enhancing accuracy and precision and potentially reducing bias in observational glomerular disease studies.<\/jats:p>","DOI":"10.1093\/ndt\/gfag194","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:48:38Z","timestamp":1787312918000},"source":"Crossref","is-referenced-by-count":0,"title":["Longitudinal eGFR data in the estimation of missing baseline eGFR in patients with glomerular disease"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5731-8974","authenticated-orcid":false,"given":"Jialin","family":"Han","sequence":"first","affiliation":[{"name":"Division of Nephrology, Department of Medicine, University of British Columbia, Vancouver, British Columbia ,","place":["Canada"]},{"name":"BC Renal, Provincial Health Services Authority , Vancouver, British Columbia ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Canney","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Ottawa and the Ottawa Hospital Research Institute , Ontario ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lee","family":"Er","sequence":"additional","affiliation":[{"name":"BC Renal, Provincial Health Services Authority , Vancouver, British Columbia ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean J","family":"Barbour","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, University of British Columbia, Vancouver, British Columbia ,","place":["Canada"]},{"name":"BC Renal, Provincial Health Services Authority , Vancouver, British Columbia ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,21]]},"reference":[{"key":"2026091405264692300_bib1","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1038\/ki.2009.243","article-title":"The Oxford classification of IgA nephropathy: rationale, clinicopathological correlations, and classification","volume":"76","author":"Cattran","year":"2009","journal-title":"Kidney Int"},{"key":"2026091405264692300_bib2","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1681\/ASN.2015111262","article-title":"Membranous nephropathy: quantifying remission duration on outcome","volume":"28","author":"Cattran","year":"2017","journal-title":"J Am Soc Nephrol"},{"key":"2026091405264692300_bib3","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1001\/jamainternmed.2019.0600","article-title":"Evaluating a new international risk-prediction tool in IgA nephropathy","volume":"179","author":"Barbour","year":"2019","journal-title":"JAMA Intern Med"},{"key":"2026091405264692300_bib4","doi-asserted-by":"publisher","first-page":"834878","DOI":"10.3389\/fimmu.2022.834878","article-title":"Assessment of renal risk score and histopathological classification for prediction of end-stage kidney disease and factors associated with change in eGFR after ANCA-glomerulonephritis diagnosis","volume":"13","author":"Brilland","year":"2022","journal-title":"Front Immunol"},{"key":"2026091405264692300_bib5","doi-asserted-by":"publisher","first-page":"1508","DOI":"10.1093\/ndt\/gfy250","article-title":"Incidence, prevalence, mortality and chronic renal damage of anti-neutrophil cytoplasmic antibody-associated glomerulonephritis in a 20-year population-based cohort","volume":"34","author":"Berti","year":"2019","journal-title":"Nephrol Dial Transplant"},{"key":"2026091405264692300_bib6","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1053\/j.ajkd.2025.11.014","article-title":"Cancer incidence in people with glomerular disease: a population-level study","volume":"87","author":"Han","year":"2026","journal-title":"Am J Kidney Dis"},{"key":"2026091405264692300_bib7","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1093\/oxfordjournals.aje.a117592","article-title":"A critical look at methods for handling missing covariates in epidemiologic regression analyses","volume":"142","author":"Greenland","year":"1995","journal-title":"Am J Epidemiol"},{"key":"2026091405264692300_bib8","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1186\/s13063-016-1473-3","article-title":"Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?","volume":"17","author":"Mukaka","year":"2016","journal-title":"Trials"},{"key":"2026091405264692300_bib9","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1093\/aje\/kwaa124","article-title":"When is a complete-case approach to missing data valid? 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