Hampson Lisa V, Jennison Christopher
Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, U.K.
Stat Med. 2015 Jan 15;34(1):39-58. doi: 10.1002/sim.6316. Epub 2014 Oct 15.
We consider seamless phase II/III clinical trials that compare K treatments with a common control in phase II then test the most promising treatment against control in phase III. The final hypothesis test for the selected treatment can use data from both phases, subject to controlling the familywise type I error rate. We show that the choice of method for conducting the final hypothesis test has a substantial impact on the power to demonstrate that an effective treatment is superior to control. To understand these differences in power, we derive decision rules maximizing power for particular configurations of treatment effects. A rule with such an optimal frequentist property is found as the solution to a multivariate Bayes decision problem. The optimal rules that we derive depend on the assumed configuration of treatment means. However, we are able to identify two decision rules with robust efficiency: a rule using a weighted average of the phase II and phase III data on the selected treatment and control, and a closed testing procedure using an inverse normal combination rule and a Dunnett test for intersection hypotheses. For the first of these rules, we find the optimal division of a given total sample size between phases II and III. We also assess the value of using phase II data in the final analysis and find that for many plausible scenarios, between 50% and 70% of the phase II numbers on the selected treatment and control would need to be added to the phase III sample size in order to achieve the same increase in power. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
我们考虑无缝的II/III期临床试验，即在II期将K种治疗方法与一个共同对照进行比较，然后在III期将最有前景的治疗方法与对照进行测试。针对所选治疗方法的最终假设检验可以使用两个阶段的数据，但需控制家族性I型错误率。我们表明，进行最终假设检验的方法选择对证明有效治疗优于对照的检验效能有重大影响。为了理解这些检验效能的差异，我们推导了针对特定治疗效果配置使检验效能最大化的决策规则。作为一个多元贝叶斯决策问题的解，找到了具有这种最优频率主义性质的规则。我们推导的最优规则取决于假设的治疗均值配置。然而，我们能够识别出两种具有稳健效率的决策规则：一种是使用所选治疗方法和对照的II期和III期数据加权平均值的规则，另一种是使用逆正态组合规则和用于交集假设的Dunnett检验的封闭检验程序。对于这些规则中的第一个，我们找到了在II期和III期之间给定总样本量的最优分配。我们还评估了在最终分析中使用II期数据的价值，发现在许多合理的情况下，为了实现相同的检验效能提高，需要将所选治疗方法和对照的II期样本量的50%至70%添加到III期样本量中。© 2014作者。《医学统计学》由John Wiley & Sons Ltd出版。
