{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T08:09:23Z","timestamp":1790150963354,"version":"4.0.1"},"reference-count":33,"publisher":"Wiley","issue":"20-22","license":[{"start":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T00:00:00Z","timestamp":1788652800000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci\u00f3n","doi-asserted-by":"publisher","award":["RYC2019\u2010027534\u2010I"],"award-info":[{"award-number":["RYC2019\u2010027534\u2010I"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci\u00f3n","doi-asserted-by":"publisher","award":["PID2023\u2010148811NB\u2010I00"],"award-info":[{"award-number":["PID2023\u2010148811NB\u2010I00"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    The identification of biomarkers with high predictive accuracy is a crucial task in medical research, as it can aid clinicians in making early decisions, thereby reducing morbidity and mortality in high\u2010risk populations. Time\u2010dependent receiver operating characteristic (ROC) curves are the main tool used to assess the accuracy of prognostic biomarkers for outcomes that evolve over time. Recognising the need to account for patient heterogeneity when evaluating the accuracy of a prognostic biomarker, we introduce a novel penalised\u2010spline\u2010based estimator of the time\u2010dependent ROC curve that accommodates a possible modifying effect of covariates. We consider flexible models for both the hazard function of the event time given the covariates and biomarker and for the location\u2010scale regression model of the biomarker given covariates, enabling the accommodation of non\u2010proportional hazards and nonlinear effects through penalised splines, thus overcoming limitations of earlier methods. The simulation study demonstrates that our approach successfully recovers the true functional form of the covariate\u2010specific time\u2010dependent ROC curve and the corresponding area under the curve across a variety of scenarios. Comparisons with existing methods further show that our approach performs favourably in multiple settings. Our approach is applied to evaluate the ability of the Global Registry of Acute Coronary Events risk score to predict mortality over different time periods after discharge in patients who have suffered an acute coronary syndrome and to investigate how this ability may vary with the left ventricular ejection fraction. An\n                    <jats:styled-content>R<\/jats:styled-content>\n                    package,\n                    <jats:styled-content>CondTimeROC<\/jats:styled-content>\n                    , implementing the proposed method is provided.\n                  <\/jats:p>","DOI":"10.1002\/sim.70718","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T01:11:56Z","timestamp":1788743516000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Penalised Spline Estimation of Covariate\u2010Specific Time\u2010Dependent ROC Curves"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1329-9238","authenticated-orcid":false,"given":"Mar\u00eda Xos\u00e9","family":"Rodr\u00edguez\u2010\u00c1lvarez","sequence":"first","affiliation":[{"name":"CITMaga and Departamento de Estat\u00edstica e Investigaci\u00f3n Operativa Universidade de Vigo  Vigo Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8084-1616","authenticated-orcid":false,"given":"Vanda","family":"In\u00e1cio","sequence":"additional","affiliation":[{"name":"School of Mathematics University of Edinburgh  Scotland UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,6]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2000.00337.x"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2005.030814.x"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201200045"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-017-0332-6"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asp002"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9848"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280217702416"},{"key":"e_1_2_9_9_1","first-page":"21","article-title":"A Review on ROC Curves in the Presence of Covariates","volume":"12","author":"Pardo\u2010Fern\u00e1ndez J. 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