{"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/semiparametric-weighted-spline-regression","title":"Semiparametric Weighted Spline Regression (SWSR) in Confirmatory Clinical Trials with Time-Varying Placebo Effects","arxiv_id":"2505.06939","date":"2025-05-11","proceeding":null,"authors":["Tianyu Zhan","Yihua Gu"],"abstract":"In confirmatory Phase 3 clinical trials with recruitment over the years, the underlying placebo effect may follow an unknown temporal trend. Taking a clinical trial on Hidradenitis Suppurativa (HS) as an example, fluctuations or variabilities are common in HS-related endpoints, mainly due to the natural disease characteristics, variations of evaluation from different physicians, and standard of care evolvement. The adjustment of time-varying placebo effects receives some attention in adaptive clinical trials and platform trials, but is usually ignored in traditional non-adaptive designs. However, under the impact of such a time drift, some existing methods may not simultaneously control the type I error rate and achieve satisfactory power. In this article, we propose SWSR (Semiparametric Weighted Spline Regression) to estimate the treatment effect with B-splines to accommodate the time-varying placebo effects nonparametrically. Our method aims to achieve the following three objectives: a proper type I error rate control under varying settings, an overall high power to detect a potential treatment effect, and robustness to unknown time-varying placebo effects. Simulation studies and a case study provide supporting evidence. Those three key features make SWSR an appealing option to be pre-specified for practical confirmatory clinical trials. Supplemental materials, including the R code, additional simulation results and theoretical discussion, are available online.","url_abs":"https://arxiv.org/abs/2505.06939v2","url_pdf":"https://arxiv.org/pdf/2505.06939v2.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":"semiparametric-weighted-spline-regression","repo_url":"https://github.com/tian-yu-zhan/SWSR","is_official":1,"mentioned_in_paper":0,"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}