{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:44:14Z","timestamp":1753875854195,"version":"3.41.2"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"content-version":"vor","delay-in-days":58,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 CA236558","U24 CA086368"],"award-info":[{"award-number":["R01 CA236558","U24 CA086368"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,4,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>Active surveillance (AS) using repeated biopsies to monitor disease progression has been a popular alternative to immediate surgical intervention in cancer care. However, a biopsy procedure is invasive and sometimes leads to severe side effects of infection and bleeding. To reduce the burden of repeated surveillance biopsies, biomarker-assistant decision rules are sought to replace the fix-for-all regimen with tailored biopsy intensity for individual patients. Constructing or evaluating such decision rules is challenging. The key AS outcome is often ascertained subject to interval censoring. Furthermore, patients will discontinue participation in the AS study once they receive a positive surveillance biopsy. Thus, patient dropout is affected by the outcomes of these biopsies. This work proposes a non-parametric kernel-based method to estimate a tailored AS strategy\u2019s true positive rates (TPRs) and true negative rates (TNRs), accounting for interval censoring and immediate dropouts. We develop a weighted classification framework based on these estimates to estimate the optimally tailored AS strategy and further incorporate the cost-benefit ratio for cost-effectiveness in medical decision-making. Theoretically, we provide a uniform generalization error bound of the derived AS strategy, accommodating all possible trade-offs between TPRs and TNRs. Simulation and application to a prostate cancer surveillance study show the superiority of the proposed method.<\/jats:p>","DOI":"10.1093\/biomtc\/ujaf067","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T13:00:51Z","timestamp":1748610051000},"source":"Crossref","is-referenced-by-count":0,"title":["Estimating optimally tailored active surveillance strategy under interval censoring"],"prefix":"10.1093","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1351-7928","authenticated-orcid":false,"given":"Muxuan","family":"Liang","sequence":"first","affiliation":[{"name":"University of Florida Department of Biostatistics, , Gainesville, FL 32611 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2851-2257","authenticated-orcid":false,"given":"Yingqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Public Health Sciences Division, Fred Hutchinson Cancer Center , Seattle, WA 98109 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel W","family":"Lin","sequence":"additional","affiliation":[{"name":"University of Washington Department of Urology, , Seattle, WA 98195 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4339-6685","authenticated-orcid":false,"given":"Matthew","family":"Cooperberg","sequence":"additional","affiliation":[{"name":"Epidemiology & Biostatistics, University of California , San Francisco, CA 94143 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3078-4200","authenticated-orcid":false,"given":"Yingye","family":"Zheng","sequence":"additional","affiliation":[{"name":"Public Health Sciences Division, Fred Hutchinson Cancer Center , Seattle, WA 98109 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"key":"2025053009004917500_bib1","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1001\/jamasurg.2017.1586","article-title":"Practice-vs physician-level variation in use of active surveillance for men with low-risk prostate cancer: implications for collaborative quality improvement","volume":"152","author":"Auffenberg","year":"2017","journal-title":"JAMA Surgery"},{"key":"2025053009004917500_bib2","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1111\/biom.13317","article-title":"Developing and evaluating risk prediction models with panel current status data","volume":"77","author":"Chan","year":"2021","journal-title":"Biometrics"},{"key":"2025053009004917500_bib3","doi-asserted-by":"crossref","first-page":"2182","DOI":"10.1200\/JCO.2015.65.7759","article-title":"Active surveillance for the management of localized prostate cancer (Cancer Care Ontario Guideline): American Society of Clinical Oncology clinical practice guideline endorsement","volume":"34","author":"Chen","year":"2016","journal-title":"Journal of Clinical Oncology"},{"key":"2025053009004917500_bib4","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.eururo.2018.01.017","article-title":"Refined analysis of prostate-specific antigen kinetics to predict prostate cancer active surveillance outcomes","volume":"74","author":"Cooperberg","year":"2018","journal-title":"European Urology"},{"key":"2025053009004917500_bib5","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1001\/jama.2015.6036","article-title":"Trends in management for patients with localized prostate cancer, 1990\u20132013","volume":"314","author":"Cooperberg","year":"2015","journal-title":"Journal of the American Medical Association"},{"key":"2025053009004917500_bib6","doi-asserted-by":"crossref","first-page":"e203187","DOI":"10.1001\/jamaoncol.2020.3187","article-title":"Tailoring intensity of active surveillance for low-risk prostate cancer based on individualized prediction of risk stability","volume":"6","author":"Cooperberg","year":"2020","journal-title":"JAMA Oncology"},{"key":"2025053009004917500_bib7","doi-asserted-by":"crossref","first-page":"3895","DOI":"10.1111\/biom.13911","article-title":"Constructing time-invariant dynamic surveillance rules for optimal monitoring schedules","volume":"79","author":"Dong","year":"2023","journal-title":"Biometrics"},{"key":"2025053009004917500_bib8","doi-asserted-by":"crossref","first-page":"591","DOI":"10.7326\/0003-4819-156-8-201204170-00010","article-title":"National Institutes of Health State-of-the-Science Conference: role of active surveillance in the management of men with localized prostate cancer","volume":"156","author":"Ganz","year":"2012","journal-title":"Annals of Internal Medicine"},{"key":"2025053009004917500_bib9","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1080\/01621459.2018.1482756","article-title":"Semiparametric regression analysis of multiple right-and interval-censored events","volume":"114","author":"Gao","year":"2019","journal-title":"Journal of the American Statistical Association"},{"key":"2025053009004917500_bib10","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1111\/j.0006-341X.2000.00940.x","article-title":"A proportional hazards model for multivariate interval-censored failure time data","volume":"56","author":"Goggins","year":"2000","journal-title":"Biometrics"},{"key":"2025053009004917500_bib11","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1214\/lnms\/1215452217","article-title":"Maximum likelihood estimation for proportional odds regression model with current status data","volume":"27","author":"Huang","year":"1995","journal-title":"Lecture Notes-Monograph Series"},{"key":"2025053009004917500_bib12","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1214\/aos\/1032894452","article-title":"Efficient estimation for the proportional hazards model with interval censoring","volume":"24","author":"Huang","year":"1996","journal-title":"The Annals of Statistics"},{"key":"2025053009004917500_bib13","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1080\/01621459.1997.10474050","article-title":"Sieve estimation for the proportional-odds failure-time regression model with interval censoring","volume":"92","author":"Huang","year":"1997","journal-title":"Journal of the American Statistical Association"},{"key":"2025053009004917500_bib14","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-74978-5","volume-title":"Introduction to Empirical Processes and Semiparametric Inference","author":"Kosorok","year":"2008"},{"key":"2025053009004917500_bib15","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1093\/biomet\/85.2.289","article-title":"Additive hazards regression with current status data","volume":"85","author":"Lin","year":"1998","journal-title":"Biometrika"},{"key":"2025053009004917500_bib16","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1002\/sim.3832","article-title":"A semiparametric probit model for case 2 interval-censored failure time data","volume":"29","author":"Lin","year":"2010","journal-title":"Statistics in Medicine"},{"key":"2025053009004917500_bib17","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1093\/biomet\/89.3.649","article-title":"Efficient estimation in additive hazards regression with current status data","volume":"89","author":"Martinussen","year":"2002","journal-title":"Biometrika"},{"key":"2025053009004917500_bib18","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1111\/biom.12562","article-title":"On longitudinal prediction with time-to-event outcome: comparison of modeling options","volume":"73","author":"Maziarz","year":"2017","journal-title":"Biometrics"},{"key":"2025053009004917500_bib19","doi-asserted-by":"crossref","first-page":"4452","DOI":"10.1002\/sim.5863","article-title":"Regression analysis for current status data using the EM algorithm","volume":"32","author":"McMahan","year":"2013","journal-title":"Statistics in Medicine"},{"key":"2025053009004917500_bib20","doi-asserted-by":"crossref","first-page":"4272","DOI":"10.1016\/j.csda.2008.02.007","article-title":"New statistical software for the proportional hazards model with current status data","volume":"52","author":"Mongou\u00e9-Tchokot\u00e9","year":"2008","journal-title":"Computational Statistics & Data Analysis"},{"key":"2025053009004917500_bib21","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1137\/1109020","article-title":"On estimating regression","volume":"9","author":"Nadaraya","year":"1964","journal-title":"Theory of Probability and Its Applications"},{"key":"2025053009004917500_bib22","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1373\/clinchem.2015.252163","article-title":"Early-phase studies of biomarkers: what target sensitivity and specificity values might confer clinical utility?","volume":"62","author":"Pepe","year":"2016","journal-title":"Clinical Chemistry"},{"key":"2025053009004917500_bib23","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1080\/10485259508832629","article-title":"A kernel estimator for discrete distributions","volume":"4","author":"Rajagopalan","year":"1995","journal-title":"Journal of Nonparametric Statistics"},{"key":"2025053009004917500_bib24","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1080\/01621459.1996.10476939","article-title":"A semiparametric proportional odds regression model for the analysis of current status data","volume":"91","author":"Rossini","year":"1996","journal-title":"Journal of the American Statistical Association"},{"key":"2025053009004917500_bib25","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.juro.2017.11.095","article-title":"Clinically localized prostate cancer: AUA\/ASTRO\/SUO guideline. Part I: risk stratification, shared decision making, and care options","volume":"199","author":"Sanda","year":"2018","journal-title":"Journal of Urology"},{"key":"2025053009004917500_bib26","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1080\/01621459.2000.10474276","article-title":"Linear regression with current status data","volume":"95","author":"Shen","year":"2000","journal-title":"Journal of the American Statistical Association"},{"key":"2025053009004917500_bib27","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1023\/A:1009652024999","article-title":"Generalized additive models for current status data","volume":"4","author":"Shiboski","year":"1998","journal-title":"Lifetime Data Analysis"},{"key":"2025053009004917500_bib28","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1093\/biomet\/93.2.329","article-title":"On the accelerated failure time model for current status and interval censored data","volume":"93","author":"Tian","year":"2006","journal-title":"Biometrika"},{"key":"2025053009004917500_bib29","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1111\/biom.12940","article-title":"Personalized schedules for surveillance of low-risk prostate cancer patients","volume":"75","author":"Tomer","year":"2019","journal-title":"Biometrics"},{"key":"2025053009004917500_bib30","first-page":"809","article-title":"Joint modeling of longitudinal and time-to-event data: an overview","volume":"14","author":"Tsiatis","year":"2004","journal-title":"Statistica Sinica"},{"key":"2025053009004917500_bib31","first-page":"614","article-title":"High-dimensional generalized linear models and the lasso","volume":"36","author":"Van\u00a0de\u00a0Geer","year":"2008","journal-title":"The Annals of Statistics"},{"key":"2025053009004917500_bib32","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1111\/j.1541-0420.2010.01532.x","article-title":"Semiparametric Bayes\u2019 proportional odds models for current status data with underreporting","volume":"67","author":"Wang","year":"2011","journal-title":"Biometrics"},{"key":"2025053009004917500_bib33","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1198\/016214507000000400","article-title":"Individual prediction in prostate cancer studies using a joint longitudinal survival\u2013cure model","volume":"103","author":"Yu","year":"2008","journal-title":"Journal of the American Statistical Association"},{"key":"2025053009004917500_bib34","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1093\/biomet\/asx029","article-title":"Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data","volume":"104","author":"Zeng","year":"2017","journal-title":"Biometrika"}],"container-title":["Biometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/biometrics\/article-pdf\/81\/2\/ujaf067\/63405802\/ujaf067.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biometrics\/article-pdf\/81\/2\/ujaf067\/63405802\/ujaf067.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T13:00:58Z","timestamp":1748610058000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biometrics\/article\/doi\/10.1093\/biomtc\/ujaf067\/8153760"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,2]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4,2]]}},"URL":"https:\/\/doi.org\/10.1093\/biomtc\/ujaf067","relation":{},"ISSN":["0006-341X","1541-0420"],"issn-type":[{"type":"print","value":"0006-341X"},{"type":"electronic","value":"1541-0420"}],"subject":[],"published-other":{"date-parts":[[2025,6]]},"published":{"date-parts":[[2025,4,2]]},"article-number":"ujaf067"}}