Pilz Maximilian, Kieser Meinhard
Institute of Medical Biometry, University of Heidelberg, Heidelberg, Germany.
Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany.
J Appl Stat. 2024 Apr 17;51(15):3178-3194. doi: 10.1080/02664763.2024.2342424. eCollection 2024.
Unblinded interim analyses in clinical trials with adaptive designs are gaining increasing popularity. Here, the type I error rate is controlled by defining an appropriate conditional error function. Since various approaches to the selection of the conditional error function exist, the question of an optimal choice arises. In this article, we extend existing work on optimal conditional error functions by two results. Firstly, we prove that techniques from variational calculus can be applied to derive existing optimal conditional error functions. Secondly, we answer the question of optimizing the conditional error function of an optimal promising zone design and investigate the efficiency gain.
在具有适应性设计的临床试验中，非盲期中分析越来越受欢迎。在此，通过定义适当的条件误差函数来控制I型错误率。由于存在多种选择条件误差函数的方法，因此出现了最优选择的问题。在本文中，我们通过两个结果扩展了关于最优条件误差函数的现有工作。首先，我们证明变分法技术可用于推导现有的最优条件误差函数。其次，我们回答了优化最优有希望区域设计的条件误差函数的问题，并研究了效率增益。
