An R Shiny App for a Chronic Lower Back Pain Study, Personalized N-of-1 Trial.
An R Shiny App for a Chronic Lower Back Pain Study, Personalized N-of-1 Trial.
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DOI：
10.1162/99608f92.6c21dab7
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发表时间：
2022
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对个性化药物的呼吁突显了个性化(N-of-1)试验的必要性，以找出对个别患者最有效的治疗方法。常规的(受试者间)随机对照试验(RCT)对普通患者产生效果，但个性化试验管理受试者内部的所有治疗，因此可以确定对单个患者的好处或损害。个性化试验的设计和分析涉及不同于传统随机对照试验的策略。这些问题包括如何调整从一个干预到另一个干预的任何结转效应，如何处理丢失的数据，以及如何为患者提供对他们的数据的洞察。此外，应该为每个患者和他们的临床医生创建一份关于试验结果的可理解的报告，以便于他们做出决策。本文描述了解决这些设计和分析问题的策略，并介绍了一个R Shiny应用程序来简化他们的解决方案，解释每种设计和统计策略的使用。为了说明，我们还提供了一个个性化试验系列的具体例子，旨在增加慢性下腰痛(CLBP)患者的活动量(即步行步数)。
The call for personalized medicine highlights the need for personalized (N-of-1) trials to find what treatment works best for individual patients. Conventional (between-subject) randomized controlled trials (RCT) yield effects for the ‘average patient,’ but a personalized trial administers all treatments within-subject, so benefits or harms to the individual patient can be identified. The design and analysis of personalized trials involve different strategies from the conventional RCT. These include how to adjust for any carryover effects from one intervention to another, how to handle missing data, and how to provide patients with insight into their data. In addition, a comprehensible report about trial results should be created for each patient and their clinician to facilitate their decision-making. This article describes strategies to address these design and analytic issues, and introduces an R shiny app to facilitate their solution, to explain the use of each of the design and statistical strategies. To illustrate, we also provide a concrete example of a personalized trial series designed to increase activity (i.e., walking steps) in patients with chronic lower back pain (CLBP).