Banerjee Anindita, Tsiatis Anastasios A
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
Stat Med. 2006 Oct 15;25(19):3382-95. doi: 10.1002/sim.2501.
Two-stage designs have been widely used in phase II clinical trials. Such designs are desirable because they allow a decision to be made on whether a treatment is effective or not after the accumulation of the data at the end of each stage. Optimal fixed two-stage designs, where the sample size at each stage is fixed in advance, were proposed by Simon when the primary outcome is a binary response. This paper proposes an adaptive two-stage design which allows the sample size at the second stage to depend on the results at the first stage. Using a Bayesian decision-theoretic construct, we derive optimal adaptive two-stage designs; the optimality criterion being minimum expected sample size under the null hypothesis. Comparisons are made between Simon's two-stage fixed design and the new design with respect to this optimality criterion.
两阶段设计已广泛应用于II期临床试验。这种设计是可取的，因为在每个阶段结束时积累数据后，可以决定一种治疗方法是否有效。当主要结果为二元反应时，Simon提出了最优固定两阶段设计，其中每个阶段的样本量提前固定。本文提出了一种自适应两阶段设计，该设计允许第二阶段的样本量取决于第一阶段的结果。使用贝叶斯决策理论结构，我们推导了最优自适应两阶段设计；最优性标准是在原假设下的最小期望样本量。就这一最优性标准而言，对Simon的两阶段固定设计和新设计进行了比较。
