{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T14:08:32Z","timestamp":1790086112907,"version":"4.0.1"},"reference-count":0,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>Restricted mean survival time offers a compelling nonparametric alternative to hazard ratios for right-censored time-to-event data, particularly when the proportional hazards assumption is violated. By capturing the total event-free time over a specified horizon, it provides an intuitive and clinically meaningful measure of absolute treatment benefit. Nonetheless, selecting the restriction time poses challenges: choosing a small restriction time may overlook late-emerging benefits, while a large one can inflate variance and reduce power, an issue whose impact on the precision of inference is often underappreciated. We propose a novel data-driven, adaptive procedure that identifies the optimal restriction time from a continuous range by maximizing a criterion balancing effect size and estimation precision. Consequently, our procedure is particularly powerful when the pattern of the treatment effect is unknown at the design stage. We provide a rigorous theoretical foundation that accounts for the additional variability introduced by adaptive selection. To address nonregular estimation under the null, we develop two complementary strategies: a convex-hull-based estimator, and a penalized approach that regularizes restriction-time selection. Additionally, when restriction time candidates are pre-specified on a discrete grid, our procedure has the same first-order distribution as an oracle estimator evaluated at the penalized population optimizer, with no additional first-order variance from selection. Extensive simulations across realistic survival scenarios demonstrate that our method outperforms traditional restricted mean survival time analyses and the log-rank test, achieving superior power while maintaining approximately nominal type I error rates. In a phase III pancreatic cancer trial with transient treatment effects, our procedure uncovers clinically meaningful benefits that standard methods overlook. Software implementing the methods is available in the package AdaRMST.<\/jats:p>","DOI":"10.1093\/biomet\/asag056","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:42:30Z","timestamp":1790077350000},"source":"Crossref","is-referenced-by-count":0,"title":["Beyond Fixed Restriction Time: Adaptive Restricted Mean Survival Time Methods in Clinical Trials"],"prefix":"10.1093","author":[{"given":"Jinghao","family":"Sun","sequence":"first","affiliation":[{"name":"Epidemiology, & Informatics, University of Pennsylvania 423 Guardian Drive Department of Biostatistics, , Philadelphia, PA 19104,","place":["U.S.A"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas E","family":"Schaubel","sequence":"additional","affiliation":[{"name":"Epidemiology, & Informatics, University of Pennsylvania 423 Guardian Drive Department of Biostatistics, , Philadelphia, PA 19104,","place":["U.S.A"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric J Tchetgen","family":"Tchetgen","sequence":"additional","affiliation":[{"name":"Epidemiology, & Informatics, University of Pennsylvania 423 Guardian Drive Department of Biostatistics, , Philadelphia, PA 19104,","place":["U.S.A"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,22]]},"container-title":["Biometrika"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/biomet\/advance-article-pdf\/doi\/10.1093\/biomet\/asag056\/71313127\/asag056.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biomet\/advance-article-pdf\/doi\/10.1093\/biomet\/asag056\/71313127\/asag056.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T13:51:34Z","timestamp":1790085094000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biomet\/advance-article\/doi\/10.1093\/biomet\/asag056\/8828005"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,22]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1093\/biomet\/asag056","relation":{},"ISSN":["0006-3444","1464-3510"],"issn-type":[{"value":"0006-3444","type":"print"},{"value":"1464-3510","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,22]]},"article-number":"asag056"}}