{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/beyond-fixed-restriction-time-adaptive","title":"Beyond Fixed Restriction Time: Adaptive Restricted Mean Survival Time Methods in Clinical Trials","arxiv_id":"2501.15284","date":"2025-01-25","proceeding":null,"authors":["Jinghao Sun","Douglas E. Schaubel","Eric J. Tchetgen Tchetgen"],"abstract":"Restricted mean survival time (RMST) 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, RMST provides an intuitive and clinically meaningful measure of absolute treatment benefit. Nonetheless, selecting the restriction time $L$ poses challenges: choosing a small $L$ can overlook late-emerging benefits, whereas a large $L$, often underestimated in its impact, may inflate variance and undermine power. We propose a novel data-driven, adaptive procedure that identifies the optimal restriction time $L^*$ from a continuous range by maximizing a criterion balancing effect size and estimation precision. Consequently, our procedure is particularly useful when the pattern of the treatment effect is unknown at the design stage. We provide a rigorous theoretical foundation that accounts for variability introduced by adaptively choosing $L^*$. To address nonregular estimation under the null, we develop two complementary strategies: a convex-hull-based estimator, and a penalized approach that further enhances power. Additionally, when restriction time candidates are defined on a discrete grid, we propose a procedure that surprisingly incurs no asymptotic penalty for selection, thus achieving oracle performance. Extensive simulations across realistic survival scenarios demonstrate that our method outperforms traditional RMST analyses and the log-rank test, achieving superior power while maintaining 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. Our methods are implemented in the R package AdaRMST.","url_abs":"https://arxiv.org/abs/2501.15284v1","url_pdf":"https://arxiv.org/pdf/2501.15284v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"beyond-fixed-restriction-time-adaptive","repo_url":"https://github.com/jinghao-sun/adarmst","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}