Biometrics, Abbott Vascular, 3200 Lakeside drive, Santa Clara, CA, 95054, USA.
Ther Innov Regul Sci. 2020 Jan;54(1):167-170. doi: 10.1007/s43441-019-00041-1. Epub 2020 Jan 6.
Adaptive design methodology has been well studied for continuous, binary, and survival outcomes for decades. However, for complicated endpoints such as recurrent hospitalization in the joint frailty setting and composite endpoint in the win-ratio setting, adaptive design is not intuitive because of sophistication in existing methods to perform sample size re-estimation.
The objective of this paper is to propose a practical generalized approach to implement the above activities at the interim stage through approximation so that sample size re-estimation becomes easily understood and readily amenable.
Through simulations on representative complex situations, the proposed method can maintain the planned statistical power by sample size re-estimation while controlling the type I error.
The proposed adaptive approach is easy to implement in general sample size re-estimation situations. Its validity can be verified through simulation under varying scenarios. In summary, this approach offers a transparent communication channel with regulatory agencies to facilitate clinical trial development regardless of the complexity of the underlying situations.
自适应设计方法已经在连续、二分类和生存结局等方面得到了广泛研究,但是对于复杂结局,如联合脆弱性环境下的复发性住院和赢比环境下的复合结局,自适应设计并不直观,因为现有的方法在进行样本量重新估计时非常复杂。
本文的目的是提出一种实用的广义方法,通过近似在中期阶段实现上述活动,以便更容易理解和方便地进行样本量重新估计。
通过对代表性复杂情况的模拟,该方法可以在控制 I 类错误的同时通过样本量重新估计来保持计划的统计效力。
所提出的自适应方法在一般的样本量重新估计情况下易于实施。其有效性可以通过在不同场景下的模拟进行验证。总之,无论基础情况的复杂性如何,该方法都为与监管机构进行透明的沟通提供了渠道,有助于临床试验的开展。