Atkinson Greg, Batterham Alan M
Health and Social Care Institute, School of Health and Social Care, Teesside University, Middlesbrough, UK.
Exp Physiol. 2015 Jun;100(6):577-88. doi: 10.1113/EP085070. Epub 2015 May 13.
What is the topic of this review? In 'personalized medicine', various plots and analyses are purported to quantify individual differences in intervention response, identify responders/non-responders and explore response moderators or mediators. What advances does it highlight? We highlight the impact of within-subject random variation, which is inevitable even with 'gold-standard' measurement tools/protocols and sometimes so substantial that it explains all apparent individual response differences. True individual response differences are quantified only by comparing the SDs of changes between intervention and comparator arms. When these SDs are similar, true individual response differences are clinically unimportant and further analysis unwarranted. Within the 'hot topic' of personalized medicine, we scrutinize common approaches for presenting and quantifying individual differences in the physiological response to an intervention. First, we explain how popular plots used to present individual differences in response are contaminated by random within-subject variation and the regression to the mean artefact. Using a simulated data set of blood pressure measurements, we show that large individual differences in physiological response can be suggested by some plots and analyses, even when the true magnitude of response is exactly the same in all individuals. Second, we present the appropriate designs and analysis approaches for quantifying the true interindividual variation in physiological response. It is imperative to include a comparator arm/condition (or derive information from a prior relevant repeatability study) to quantify true interindividual differences in response. The most important statistic is the SD of changes in the intervention arm, which should be compared with the same SD in the comparator arm or from a prior repeatability study in the same population conducted over the same duration as the particular intervention. Only if the difference between these SDs is clinically relevant is it logical to go on to explore any moderators or mediators of the intervention effect that might explain the individual response. To date, very few researchers have compared these SDs before making claims about individual differences in physiological response and their importance to personalized medicine.
这篇综述的主题是什么？在“个性化医疗”中，各种图表和分析旨在量化干预反应中的个体差异，识别反应者/无反应者，并探索反应调节因素或介导因素。它突出了哪些进展？我们强调了个体内部随机变异的影响，即使使用“金标准”测量工具/方案，这种变异也是不可避免的，有时甚至非常大，以至于它解释了所有明显的个体反应差异。只有通过比较干预组和对照臂之间变化的标准差，才能量化真正的个体反应差异。当这些标准差相似时，真正的个体反应差异在临床上并不重要，无需进一步分析。在“个性化医疗”这个“热门话题”中，我们仔细研究了呈现和量化干预生理反应个体差异的常用方法。首先，我们解释了用于呈现反应个体差异的流行图表是如何受到个体内部随机变异和均值回归假象的影响的。通过一个模拟的血压测量数据集，我们表明，即使所有个体的真实反应幅度完全相同，一些图表和分析也可能显示出生理反应上的巨大个体差异。其次，我们介绍了量化生理反应中真正个体间变异的适当设计和分析方法。必须包括一个对照臂/条件（或从先前相关的重复性研究中获取信息），以量化反应中真正的个体间差异。最重要的统计量是干预组变化的标准差，应将其与对照臂中的相同标准差或在与特定干预相同持续时间内对同一人群进行的先前重复性研究中的标准差进行比较。只有当这些标准差之间的差异具有临床相关性时，继续探索可能解释个体反应的干预效果的任何调节因素或介导因素才是合理的。迄今为止，很少有研究人员在声称生理反应的个体差异及其对个性化医疗的重要性之前，比较过这些标准差。
