Bischoff Wolfgang, Miller Frank
Faculty of Mathematics and Geography, Catholic University of Eichstatt-Ingolstadt, Eichstatt, Germany.
J Biopharm Stat. 2009 Jul;19(4):595-609. doi: 10.1080/10543400902963193.
A main objective in clinical trials is to find the best treatment in a given finite class of competing treatments and then to show superiority of this treatment against a control treatment. Traditionally, the best treatment is estimated in a phase II trial. Then in an independent phase III trial, superiority of this treatment, estimated as best in the first trial, is to be shown against the control treatment by a size alpha test. In this paper we investigate a competing adaptive two-stage test procedure for a seamless phase II/III trial. We assume that the variance is unknown and include therefore the calculation of the total sample size based on the first-stage-variance estimation. We derive formulae for the expected number of patients. These formulae depend on the unknown variance only, not on the other unknown parameters. Using a prior for the unknown variance, we can determine the two-stage test procedure of size alpha and power 1 - beta that is optimal in that it needs a minimal number of observations. The results are illustrated by a numerical example that indicates the superiority of the adaptive procedure over the traditional approach.
临床试验的一个主要目标是在给定的有限类竞争性治疗方法中找到最佳治疗方法，然后证明该治疗方法相对于对照治疗的优越性。传统上，最佳治疗方法是在II期试验中估计出来的。然后，在一项独立的III期试验中，要通过规模为α的检验来证明在第一项试验中被估计为最佳的这种治疗方法相对于对照治疗的优越性。在本文中，我们研究了一种用于无缝II/III期试验的竞争性自适应两阶段检验程序。我们假设方差未知，因此包括基于第一阶段方差估计来计算总样本量。我们推导了患者预期数量的公式。这些公式仅取决于未知方差，而不取决于其他未知参数。使用未知方差的先验分布，我们可以确定规模为α且功效为1-β的两阶段检验程序，该程序是最优的，因为它需要最少的观察次数。通过一个数值示例说明了结果，该示例表明了自适应程序相对于传统方法的优越性。
