An Improved Bland-Altman Method for Concordance Assessment

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-01-06 DOI:10.2202/1557-4679.1295

Jason J. Z. Liao, R. Capen

引用次数: 10

Abstract

It is often necessary to compare two measurement methods in medicine and other experimental sciences. This problem covers a broad range of data with applications arising from many different fields. The Bland-Altman method has been a favorite method for concordance assessment. However, the Bland-Altman approach creates a problem of interpretation for many applications when a mixture of fixed bias, proportional bias and/or proportional error occurs. In this paper, an improved Bland-Altman method is proposed to handle more complicated scenarios in practice. This new approach includes Bland-Altman's approach as its special case. We evaluate concordance by defining an agreement interval for each individual paired observation and assessing the overall concordance. The proposed interval approach is very informative and offers many advantages over existing approaches. Data sets are used to demonstrate the advantages of the new method.

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一种改进的Bland-Altman一致性评价方法

在医学和其他实验科学中，经常需要比较两种测量方法。这个问题涉及的数据范围很广，应用程序来自许多不同的领域。Bland-Altman方法一直是一致性评估的常用方法。然而，当固定偏差、比例偏差和/或比例误差混合出现时，Bland-Altman方法在许多应用中产生了解释问题。本文提出了一种改进的Bland-Altman方法来处理实际中更复杂的场景。这种新方法将布兰德-奥特曼的方法作为特例。我们通过定义每个个体配对观察的一致间隔和评估整体一致性来评估一致性。所提出的区间方法信息量很大，与现有方法相比具有许多优点。数据集被用来证明新方法的优点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.