Biometrics and Data Science, Bristol Myers Squibb, Boudry, Switzerland.
Pharm Stat. 2021 May;20(3):645-656. doi: 10.1002/pst.2098. Epub 2021 Feb 14.
In a clinical trial, sometimes it is desirable to allocate as many patients as possible to the best treatment, in particular, when a trial for a rare disease may contain a considerable portion of the whole target population. The Gittins index rule is a powerful tool for sequentially allocating patients to the best treatment based on the responses of patients already treated. However, its application in clinical trials is limited due to technical complexity and lack of randomness. Thompson sampling is an appealing approach, since it makes a compromise between optimal treatment allocation and randomness with some desirable optimal properties in the machine learning context. However, in clinical trial settings, multiple simulation studies have shown disappointing results with Thompson samplers. We consider how to improve short-run performance of Thompson sampling and propose a novel acceleration approach. This approach can also be applied to situations when patients can only be allocated by batch and is very easy to implement without using complex algorithms. A simulation study showed that this approach could improve the performance of Thompson sampling in terms of average total response rate. An application to a redesign of a preference trial to maximize patient's satisfaction is also presented.
在临床试验中，有时希望将尽可能多的患者分配到最佳治疗方案中，特别是当针对罕见病的试验可能包含整个目标人群的相当一部分时。吉廷斯指数规则是一种根据已治疗患者的反应，为最佳治疗方案进行序贯患者分配的强大工具。然而，由于技术复杂性和缺乏随机性，其在临床试验中的应用受到限制。汤普森抽样是一种有吸引力的方法，因为它在机器学习上下文中在最佳治疗分配和随机性之间进行了权衡，并具有一些理想的最佳特性。然而，在临床试验环境中，多项模拟研究表明汤普森抽样器的结果令人失望。我们考虑如何提高汤普森抽样的短期性能，并提出一种新的加速方法。该方法还可应用于患者只能分批分配的情况，并且无需使用复杂算法即可非常轻松地实现。一项模拟研究表明，该方法可以提高汤普森抽样的平均总响应率方面的性能。还介绍了将其应用于重新设计偏好试验以最大化患者满意度的情况。
