{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:26:59Z","timestamp":1787498819244,"version":"build-2736575974"},"reference-count":31,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T00:00:00Z","timestamp":1713398400000},"content-version":"vor","delay-in-days":17,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biometrical J"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In order to assess prognostic risk for individuals in precision health research, risk prediction models are increasingly used, in which statistical models are used to estimate the risk of future outcomes based on clinical and nonclinical characteristics. The predictive accuracy of a risk score must be assessed before it can be used in routine clinical decision making, where the receiver operator characteristic curves, precision\u2013recall curves, and their corresponding area under the curves are commonly used metrics to evaluate the discriminatory ability of a continuous risk score. Among these the precision\u2013recall curves have been shown to be more informative when dealing with unbalanced biomarker distribution between classes, which is common in rare event, even though except one, all existing methods are proposed for classic uncensored data. This paper is therefore to propose a novel nonparametric estimation approach for the time\u2010dependent precision\u2013recall curve and its associated area under the curve for right\u2010censored data. A simulation is conducted to show the better finite sample property of the proposed estimator over the existing method and a real\u2010world data from primary biliary cirrhosis trial is used to demonstrate the practical applicability of the proposed\u00a0estimator.<\/jats:p>","DOI":"10.1002\/bimj.202300135","type":"journal-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T22:40:30Z","timestamp":1713480030000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A novel nonparametric time\u2010dependent precision\u2013recall curve estimator for right\u2010censored survival data"],"prefix":"10.1002","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2067-6054","authenticated-orcid":false,"given":"Kassu Mehari","family":"Beyene","sequence":"first","affiliation":[{"name":"College of Health Solutions Arizona State University Phoenix Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3199-8665","authenticated-orcid":false,"given":"Ding\u2010Geng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Health Solutions Arizona State University Phoenix Arizona USA"},{"name":"Department of Statistics University of Pretoria Pretoria South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yehenew Getachew","family":"Kifle","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics University of Maryland Baltimore County Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,4,18]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"Beran R.(1981).Nonparametric regression with randomly censored survival data.Technical report University of California Berkeley."},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8671"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.202000382"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201800376"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.5958"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201200045"},{"key":"e_1_2_9_8_1","doi-asserted-by":"crossref","unstructured":"Brodersen K. H. Ong C. S. Stephan K. E. &Buhmann J. M.(2010).The binormal assumption on precision\u2010recall curves. InJ. E.Guerrero(Ed.) 2010 20th international conference on pattern recognition(pp.4263\u20134266).IEEE.","DOI":"10.1109\/ICPR.2010.1036"},{"key":"e_1_2_9_9_1","unstructured":"Cai H. Yuan Y. Zhou Q. M. &Li B.(2018).APtools: Average positive predictive values (AP) for binary outcomes and censored event times.https:\/\/CRAN.R\u2010project.org\/package=APtools. R package version 6.8.8."},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"Davis J. &Goadrich M.(2006).The relationship between precision\u2010recall and ROC curves. InW. W.Cohen &A.Moore(Eds.) Proceedings of the 23rd international conference on machine learning(pp.233\u2013240).Association for Computing Machinery New York NY United States.","DOI":"10.1145\/1143844.1143874"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176344552"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X9901900303"},{"key":"e_1_2_9_13_1","volume-title":"Counting processes and survival analysis","author":"Fleming T. R.","year":"2011"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2000.00337.x"},{"key":"e_1_2_9_15_1","unstructured":"Heagerty P. J. &packaging by Paramita Saha\u2010Chaudhuri. (2022).Survival ROC: Time\u2010dependent ROC curve estimation from censored survival data.https:\/\/CRAN.R\u2010project.org\/package=survivalROC. 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