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Using multiple agreement methods for continuous repeated measures data: a tutorial for practitioners

机译:使用多种协议方法进行连续重复措施数据:从业者的教程

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摘要

Abstract Background Studies of agreement examine the distance between readings made by different devices or observers measuring the same quantity. If the values generated by each device are close together most of the time then we conclude that the devices agree. Several different agreement methods have been described in the literature, in the linear mixed modelling framework, for use when there are time-matched repeated measurements within subjects. Methods We provide a tutorial to help guide practitioners when choosing among different methods of assessing agreement based on a linear mixed model assumption. We illustrate the use of five methods in a head-to-head comparison using real data from a study involving Chronic Obstructive Pulmonary Disease (COPD) patients and matched repeated respiratory rate observations. The methods used were the concordance correlation coefficient, limits of agreement, total deviation index, coverage probability, and coefficient of individual agreement. Results The five methods generated similar conclusions about the agreement between devices in the COPD example; however, some methods emphasized different aspects of the between-device comparison, and the interpretation was clearer for some methods compared to others. Conclusions Five different methods used to assess agreement have been compared in the same setting to facilitate understanding and encourage the use of multiple agreement methods in practice. Although there are similarities between the methods, each method has its own strengths and weaknesses which are important for researchers to be aware of. We suggest that researchers consider using the coverage probability method alongside a graphical display of the raw data in method comparison studies. In the case of disagreement between devices, it is important to look beyond the overall summary agreement indices and consider the underlying causes. Summarising the data graphically and examining model parameters can both help with this.
机译:摘要协议背景研究检查不同设备或观察者测量相同量的读数之间的距离。如果每个设备生成的值大部分时间都靠近在一起,那么我们得出结论,设备同意。在线性混合建模框架中,在文献中描述了几种不同的协议方法,用于在受试者内有时间匹配的重复测量时使用。方法提供教程,帮助指导从业者在根据线性混合模型假设评估协议的不同方法时。我们说明了使用来自涉及慢性阻塞性肺病(COPD)患者的实际数据并匹配重复呼吸速率观察的实际数据在头脑比较中使用五种方法。使用的方法是一致的相关系数,协议限制,偏差指数,覆盖概率和个人协议系数。结果五种方法对COPD示例中的设备之间的协议产生了类似的结论;然而,一些方法强调了设备之间的不同方面,并且与他人相比,解释是一些方法的。结论在相同的环境中比较了用于评估协议的五种不同方法,以便于理解和鼓励在实践中使用多种协议方法。虽然这些方法之间存在相似之处,但是每个方法都有自己的优势和缺点,这对研究人员来说很重要。我们建议研究人员考虑使用覆盖概率方法以及方法比较研究中的原始数据的图形显示。在对设备之间分歧的情况下,重要的是要超越整体摘要协议指数,并考虑潜在的原因。总结图形和检查模型参数的数据都可以帮助解决问题。

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