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首页> 外文期刊>Journal of applied statistics >Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints
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Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints

机译：使用Gittins索引的基于贝叶斯自适应强盗的设计，用于具有正态分布端点的多臂试验

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摘要

Adaptive designs for multi-armed clinical trials have become increasingly popular recently because of their potential to shorten development times and to increase patient response. However, developing response-adaptive designs that offer patient-benefit while ensuring the resulting trial provides a statistically rigorous and unbiased comparison of the different treatments included is highly challenging. In this paper, the theory of Multi-Armed Bandit Problems is used to define near optimal adaptive designs in the context of a clinical trial with a normally distributed endpoint with known variance. We report the operating characteristics (type I error, power, bias) and patient-benefit of these approaches and alternative designs using simulation studies based on an ongoing trial. These results are then compared to those recently published in the context of Bernoulli endpoints. Many limitations and advantages are similar in both cases but there are also important differences, specially with respect to type I error control. This paper proposes a simulation-based testing procedure to correct for the observed type I error inflation that bandit-based and adaptive rules can induce.
机译：用于多臂临床试验的自适应设计最近因其缩短开发时间和增加患者反应的潜力而变得越来越流行。但是，开发能够在确保最终试验能够对所包括的不同治疗方法进行统计学上严格且无偏见的比较的同时提供患者受益的适应性设计是非常具有挑战性的。在本文中，多武装强盗问题的理论用于在具有正态分布且已知方差的端点的临床试验中定义近乎最佳的自适应设计。我们基于正在进行的试验，通过模拟研究报告了这些方法和替代设计的操作特性（I型误差，功率，偏差）和患者受益。然后将这些结果与最近在Bernoulli端点的上下文中发表的结果进行比较。在这两种情况下，许多限制和优点是相似的，但是也存在重要的区别，尤其是在类型I错误控制方面。本文提出了一种基于模拟的测试程序，以纠正基于强盗和自适应规则可能引起的I型误差膨胀。

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