{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T14:42:06Z","timestamp":1786372926574,"version":"build-2736575974"},"reference-count":15,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"9","funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"crossref","award":["U01DK103225"],"award-info":[{"award-number":["U01DK103225"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"crossref","award":["K08 DK115891-01"],"award-info":[{"award-number":["K08 DK115891-01"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The Nephrotic Syndrome Study Network Consortium","award":["U54-DK-083912"],"award-info":[{"award-number":["U54-DK-083912"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CJASN"],"published-print":{"date-parts":[[2019,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background and objectives<\/jats:title>\n                    <jats:p>Identifying novel biomarkers is critical to advancing diagnosis and treatment of CKD, but relies heavily on the statistical methods used. Inappropriate methods can lead to both false positive and false negative associations between biomarkers and outcomes. This study assessed accuracy of methods using computer simulations and compared biomarker association estimates in the NEPhrotic syndrome sTUdy NEtwork (NEPTUNE), a prospective cohort study of patients with glomerular disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design, setting, participants, &amp; measurements<\/jats:title>\n                    <jats:p>\n                      We compared three methods for analyzing repeatedly measured biomarkers in proportional hazards models: (\n                      <jats:italic toggle=\"yes\">1<\/jats:italic>\n                      ) time-invariant average, that averages values over all follow-up and uses the average as a baseline covariate, (\n                      <jats:italic toggle=\"yes\">2<\/jats:italic>\n                      ) time-varying last observation carried forward (LOCF), that assumes the covariate is unchanged until the next observed value, and (\n                      <jats:italic toggle=\"yes\">3<\/jats:italic>\n                      ) time-varying cumulative average, that updates the average using values at or before each measurement.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Under both true mechanisms of LOCF and cumulative average, simulation results showed the time-invariant average method often gave extremely inaccurate results. When LOCF was the true association mechanism, the cumulative average method often gave overestimated association estimates that were further away from the null. When cumulative average was the true mechanism, LOCF always underestimated the associations,\n                      <jats:italic toggle=\"yes\">i.e.<\/jats:italic>\n                      , closer to the null. In NEPTUNE, compared with the LOCF or cumulative average methods, hazard ratios estimated from the time-invariant average method were always higher.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Different analytic methods resulted in markedly different results. Using the time-invariant average produces inaccurate association estimates, whereas other methods can estimate additive (cumulative average) or instantaneous (LOCF) associations depending on the hypothesized underlying association mechanism and research question.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2215\/cjn.00450119","type":"journal-article","created":{"date-parts":[[2019,8,15]],"date-time":"2019-08-15T16:25:14Z","timestamp":1565886314000},"page":"1315-1323","source":"Crossref","is-referenced-by-count":7,"title":["Methods for Assessing Longitudinal Biomarkers of Time-to-Event Outcomes in CKD"],"prefix":"10.2215","volume":"14","author":[{"given":"Qian","family":"Liu","sequence":"first","affiliation":[{"name":"Arbor Research Collaborative for Health, Ann Arbor, Michigan; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abigail R.","family":"Smith","sequence":"additional","affiliation":[{"name":"Arbor Research Collaborative for Health, Ann Arbor, Michigan; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura H.","family":"Mariani","sequence":"additional","affiliation":[{"name":"Arbor Research Collaborative for Health, Ann Arbor, Michigan; and"},{"name":"Michigan Medicine, University of Michigan, Ann Arbor, Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viji","family":"Nair","sequence":"additional","affiliation":[{"name":"Michigan Medicine, University of Michigan, Ann Arbor, Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jarcy","family":"Zee","sequence":"additional","affiliation":[{"name":"Arbor Research Collaborative for Health, Ann Arbor, Michigan; and"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2019,8,15]]},"reference":[{"key":"B1-20230813","doi-asserted-by":"crossref","first-page":"316ra193","DOI":"10.1126\/scitranslmed.aac7071","article-title":"Tissue transcriptome-driven identification 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