PDF(Pubmed)
Abstract：
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).
摘要：
个性化医疗的呼吁强调了个性化（N-of-1）试验的必要性，以找到最适合个体患者的治疗方法。常规（受试者间）随机对照试验（RCT）对普通患者产生影响，但个性化试验管理受试者内的所有治疗，因此可以确定对个体患者的益处或危害。个性化试验的设计和分析涉及与常规RCT不同的策略。这些包括如何调整从一种干预到另一种干预的任何遗留影响，如何处理丢失的数据，以及如何为患者提供深入了解他们的数据。此外,应该为每位患者及其临床医生创建一份易于理解的试验结果报告,以便于他们做出决策.本文介绍了解决这些设计和分析问题的策略，并介绍了一个R闪亮的应用程序来促进他们的解决方案，解释每个设计和统计策略的使用。为了说明，我们还提供了一个旨在增加活动的个性化试验系列的具体示例(即，步行步骤）慢性下背痛（CLBP）患者。
