The conventional phase II trial design paradigm is to make the go/no-go decision based on the hypothesis testing framework. Statistical significance itself alone, however, may not be sufficient to establish that the drug is clinically effective enough to warrant confirmatory phase III trials. We propose the Bayesian optimal phase II trial design with dual-criterion decision making (BOP2-DC), which incorporates both statistical significance and clinical relevance into decision making. Based on the posterior probability that the treatment effect reaches the lower reference value (statistical significance) and the clinically meaningful value (clinical significance), BOP2-DC allows for go/consider/no-go decisions, rather than a binary go/no-go decision, and it is optimized to maximize the probability of a go decision when the treatment is effective or minimize the sample size when the treatment is futile. BOP2-DC is highly flexible and accommodates various types of endpoints, including binary, continuous, time-to-event, multiple, and co-primary endpoints, in single-arm and randomized trials. Simulation studies show that the BOP2-DC design yields desirable operating characteristics. The software to implement BOP2-DC is freely available at \url{www.trialdesign.org}.

翻译：常规的第二阶段试验设计范式是,根据假设测试框架作出上/不-go决定; 然而,单凭统计意义本身本身可能不足以确定该药物具有临床效力,足以进行第三阶段确认性试验; 我们提议采用巴伊西亚最佳的第二阶段试验设计,同时作出双重标准决策(BOP2-DC),其中既包括统计意义,也包括临床相关性,将统计意义纳入决策; 根据治疗效果达到较低参考值(统计意义)和临床有意义的值(临床意义)的事后概率,BOP2-DC允许作出G/考虑/no-go决定,而不是二进制决定/no-go决定; 我们建议,在治疗有效或当治疗无效时将样本大小减至最小时,采用最佳的Biesians 第二阶段试验设计; BOP2-DC非常灵活,并适应各种类型的终点,包括二进制、连续、时间-活动、多重和共同-初级终点,在单一武器和随机化试验中; 模拟研究显示,BOP2-DC设计具有理想的特性。