随机临床试验中具有非比例风险的无效监测：最优条件功效方法。
Futility monitoring for randomized clinical trials with non-proportional hazards: An optimal conditional power approach.
机构信息
Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
出版信息
Clin Trials. 2023 Dec;20(6):603-612. doi: 10.1177/17407745231181908. Epub 2023 Jun 27.
BACKGROUND
Standard futility analyses designed for a proportional hazards setting may have serious drawbacks when non-proportional hazards are present. One important type of non-proportional hazards occurs when the treatment effect is delayed. That is, there is little or no early treatment effect but a substantial later effect.
METHODS
We define optimality criteria for futility analyses in this setting and propose simple search procedures for deriving such rules in practice.
RESULTS
We demonstrate the advantages of the optimal rules over commonly used rules in reducing the average number of events, the average sample size, or the average study duration under the null hypothesis with minimal power loss under the alternative hypothesis.
CONCLUSION
Optimal futility rules can be derived for a non-proportional hazards setting that control the loss of power under the alternative hypothesis while maximizing the gain in early stopping under the null hypothesis.
背景
为比例风险设定设计的标准无效性分析在存在非比例风险时可能存在严重缺陷。一种重要的非比例风险发生在治疗效果延迟时。也就是说，早期治疗效果很小或没有，但后期效果很大。
方法
我们在这种情况下定义了无效性分析的最优标准，并提出了简单的搜索程序，以便在实践中得出这些规则。
结果
我们证明了最优规则相对于常用规则的优势，即在零假设下，最优规则可以通过减少平均事件数、平均样本量或平均研究持续时间来减少无效性，而在替代假设下最小化了功率损失。
结论
可以为非比例风险设定推导最优无效性规则，在控制替代假设下的功率损失的同时，最大化零假设下的早期停止增益。
Zotero 插件