阶段(地层学)
管道(软件)
二项分布
样本量测定
统计
提前停车
边距(机器学习)
计算机科学
负二项分布
I类和II类错误
统计能力
数学
人工智能
机器学习
泊松分布
生物
人工神经网络
古生物学
程序设计语言
作者
Bo Chen,Xing Zhao,Juying Zhang
摘要
Abstract Two‐stage single arm designs are widely used in phase II clinical trials with binary endpoints. The trial may be stopped early due to insufficient positive responses in the first stage. There may be some enrolled subjects who have yet to respond by the end of the first stage, and their data are ignored if the first stage results in rejection of the trial. It is possible that the result after the first stage is rejection by a slim margin, while the results of pipeline subjects are quite positive. In this case, combining the data from the two sources may provide a valuable opportunity to rescue a promising treatment that was mistakenly rejected. We propose a novel double‐check design to take advantage of the pipeline subjects' data to establish a rescue criterion based on two‐stage design. When the rescue criterion is met, the decision to reject the trial at the end of the first stage can be reversed, allowing the trial to continue. A derivation based on a binomial distribution shows that the double‐check strategy can strictly preserve the type I error rate. Further examination shows that the strategy can provide a slight increase in overall power and a substantial increase in conditional power when the proportion of positive response at the end of the first stage is at the margin. The extra rescue opportunity's cost is pretty low, only a slight increasing in the expected sample size.
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