{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:50:51Z","timestamp":1777881051200,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Mekelle University, Mekelle Tigray, Ethiopia"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["SAGE Open Medicine"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Chronic kidney disease, or renal failure, is a public health problem with an estimated prevalence of 8%\u201316% worldwide. This study was conducted to investigate the evolution of hematocrit levels over time in renal patients after transplantation and to determine how the evolution of hematocrit levels depends on the patients\u2019 age, sex, and other factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>The main objective of this study was to employ a mixed-effects model to examine the unbalanced longitudinal evolution of hematocrit levels in chronic kidney failure patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology:<\/jats:title>\n                    <jats:p>This longitudinal study included 1160 patients who received a renal transplant. These patients were followed for at most 10\u2009years. The hematocrit level was considered the response, while the covariates were time in years, sex, and age of the patients. Different statistical methods, such as explanatory analysis, multivariate regression, two-stage analysis, and linear mixed-effects models, were employed to explore the evolution of hematocrit over time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The results revealed that hematocrit levels in kidney transplant patients evolve. The sex and age of the patient significantly affect the evolution of hematocrit levels. Males tend to have a greater increase in hematocrit levels over time than females do. Hematocrit levels tend to increase with increasing age. Furthermore, cardiovascular problems before transplant and rejection symptoms did not significantly affect the evolution of hematocrit levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Hematocrit levels evolve, and this evolution follows a quartic time effect. The change in hematocrit levels varies according to the sex and age of the patient after a kidney transplant. Patients with low hematocrit levels tend to have a greater increase over time.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20503121251360864","type":"journal-article","created":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T07:28:28Z","timestamp":1757748508000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A mixed-effect model for the evolution of unbalanced longitudinal hematocrit levels in chronic kidney failure patients"],"prefix":"10.1177","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1347-6994","authenticated-orcid":false,"given":"Yemane Hailu","family":"Fissuh","sequence":"first","affiliation":[{"name":"Biostatistics, Department of Statistics, College of Natural and Computational Sciences, Aksum University, Axum, Tigray, Ethiopia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Getachew Beyene","family":"Nega","sequence":"additional","affiliation":[{"name":"Department of Statistics, College of Natural and Computational Sciences, Mekelle University, Tigray, Ethiopia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azmera","family":"Hailay","sequence":"additional","affiliation":[{"name":"Department of Statistics, College of Natural and Computational Sciences, Mekelle University, Tigray, Ethiopia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,9,13]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.14745"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)60687-X"},{"key":"e_1_3_3_4_2","article-title":"Longitudinal modeling of hematocrit levels in chronic kidney disease under mixed effect models","author":"Green D","year":"2020","unstructured":"Green D, et al. Longitudinal modeling of hematocrit levels in chronic kidney disease under mixed effect models. J Nephrol 2020.","journal-title":"J Nephrol"},{"issue":"3","key":"e_1_3_3_5_2","first-page":"698","article-title":"Statistical approaches for analyzing unbalanced longitudinal data in renal research","volume":"6","author":"Smith J","year":"2021","unstructured":"Smith J, et al. Statistical approaches for analyzing unbalanced longitudinal data in renal research. Kidney Int Rep 2021; 6(3): 698\u2013705.","journal-title":"Kidney Int Rep"},{"key":"e_1_3_3_6_2","unstructured":"Winterbottom J. A cross-sectional correlational survey to explore the relationship between Renal Association biochemical and haematological markers and health-related quality of life in patients receiving haemodialysis in the North West of England. Doctoral Dissertation University of Manchester Manchester UK 2015."},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12882-022-02920-6"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-141-2-200407200-00007"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44199-024-00085-9"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1046\/j.1523-1755.2003.00850.x"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12882-024-03869-4"},{"issue":"2","key":"e_1_3_3_12_2","article-title":"Mixed-effects model: a useful statistical tool for longitudinal and cluster studies","volume":"49","author":"Silveira LTYD","year":"2023","unstructured":"Silveira LTYD, Ferreira JC, Patino CM. Mixed-effects model: a useful statistical tool for longitudinal and cluster studies. J Bras Pneumol 2023; 49(2): e20230137.","journal-title":"J Bras Pneumol"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8282"},{"key":"e_1_3_3_14_2","unstructured":"Shiel CW. Hematocrit http:\/\/www.medicinenet.com\/hematocrit\/article.htm (2015 accessed 26 October 2016)."},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/cvr\/cvad083"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines12030622"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.17944\/interdiscip.1462456"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0230980"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1111\/stan.12188"},{"key":"e_1_3_3_20_2","first-page":"267","volume-title":"2nd International symposium on information theory","author":"Akaike H","year":"1973","unstructured":"Akaike H. Information theory and an extension of the maximum likelihood principle. 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