Pilz Maximilian, Kunzmann Kevin, Herrmann Carolin, Rauch Geraldine, Kieser Meinhard
Institute of Medical Biometry and Informatics, University Medical Center Ruprecht-Karls University Heidelberg, Heidelberg, Germany.
Institute of Biometry and Clinical Epidemiology, Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany.
Stat Med. 2019 Sep 20;38(21):4159-4171. doi: 10.1002/sim.8291. Epub 2019 Jul 1.
Recalculating the sample size in adaptive two-stage designs is a well-established method to gain flexibility in a clinical trial. Jennison and Turnbull (2015) proposed an "optimal" adaptive two-stage design based on the inverse normal combination test, which minimizes a mixed criterion of expected sample size under the alternative and conditional power. We demonstrate that the use of a combination test is not necessary to control the type one error rate and use variational techniques to develop a general adaptive design that is globally optimal under predefined optimality criteria. This approach yields to more efficient designs and furthermore allows to investigate the efficiency of the inverse normal method and the relation between local (interim-based) recalculation rules and global (unconditional) optimality of adaptive two-stage designs.
在适应性两阶段设计中重新计算样本量是在临床试验中获得灵活性的一种成熟方法。詹尼森和特恩布尔（2015年）基于逆正态组合检验提出了一种“最优”适应性两阶段设计，该设计在备择假设和条件检验效能下将预期样本量的混合准则最小化。我们证明，控制一类错误率并不一定需要使用组合检验，并且使用变分技术来开发一种在预定义最优性准则下全局最优的通用适应性设计。这种方法产生了更有效的设计，此外还允许研究逆正态方法的效率以及适应性两阶段设计的局部（基于期中分析）重新计算规则与全局（无条件）最优性之间的关系。
