Semiparametric Weighted Spline Regression (SWSR) in Confirmatory Clinical Trials with Time-Varying Placebo Effects

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.

在历时多年招募的确证性3期临床试验中，潜在的安慰剂效应可能呈现未知的时间趋势。以化脓性汗腺炎（Hidradenitis Suppurativa, HS）临床试验为例，HS相关终点指标的波动或变异较为常见，主要归因于疾病的自然特征、不同医师评估的差异以及护理标准的演变。时变安慰剂效应的调整在适应性临床试验和平台试验中已受到一定关注，但在传统非适应性设计中通常被忽视。然而，在这种时间漂移的影响下，部分现有方法可能无法同时控制I类错误率（Type I error rate）并获得满意的检验效能。本文提出半参数加权样条回归（SWSR, Semiparametric Weighted Spline Regression）方法，通过B样条非参数地拟合时变安慰剂效应，以估计治疗效应。该方法旨在实现以下三个目标：在不同设置下恰当控制I类错误率、具备检测潜在治疗效应的整体高检验效能，以及对未知时变安慰剂效应的稳健性。模拟研究和案例研究提供了支持证据。这三个关键特征使SWSR成为实际确证性临床试验中预先指定的理想选择。补充材料（包括R代码、额外模拟结果及理论讨论）可在线获取。