Mander Adrian P, Wason James M S, Sweeting Michael J, Thompson Simon G
MRC Biostatistics Unit Hub in Trials Methodology Research, Institute of Public Health, University Forvie Site, Cambridge CB2 0SR, UK.
Pharm Stat. 2012 Mar-Apr;11(2):91-6. doi: 10.1002/pst.501. Epub 2012 Jan 10.
Two-stage studies may be chosen optimally by minimising a single characteristic like the maximum sample size. However, given that an investigator will initially select a null treatment effect and the clinically relevant difference, it is better to choose a design that also considers the expected sample size for each of these values. The maximum sample size and the two expected sample sizes are here combined to produce an expected loss function to find designs that are admissible. Given the prior odds of success and the importance of the total sample size, minimising the expected loss gives the optimal design for this situation. A novel triangular graph to represent the admissible designs helps guide the decision-making process. The H₀-optimal, H₁-optimal, H₀-minimax and H₁-minimax designs are all particular cases of admissible designs. The commonly used H₀-optimal design is rarely good when allowing stopping for efficacy. Additionally, the δ-minimax design, which minimises the maximum expected sample size, is sometimes admissible under the loss function. However, the results can be varied and each situation will require the evaluation of all the admissible designs. Software to do this is provided.
两阶段研究可以通过最小化单个特征（如最大样本量）来进行最优选择。然而，考虑到研究者最初会选择一个无效治疗效应和临床相关差异，最好选择一种同时考虑这些值各自预期样本量的设计。这里将最大样本量和两个预期样本量结合起来，以产生一个预期损失函数，从而找到可接受的设计。鉴于成功的先验概率和总样本量的重要性，最小化预期损失可得出这种情况下的最优设计。一种用于表示可接受设计的新型三角图有助于指导决策过程。H₀最优、H₁最优、H₀极小极大和H₁极小极大设计都是可接受设计的特殊情况。常用的H₀最优设计在允许因疗效而停止试验时很少是理想的。此外，最小化最大预期样本量的δ极小极大设计有时在损失函数下是可接受的。然而，结果可能会有所不同，每种情况都需要对所有可接受设计进行评估。本文提供了用于此目的的软件。
