Center for Primary Care and Public Health (unisanté), DFRI/Division of Biostatistics, University of Lausanne, Switzerland.
J Clin Epidemiol. 2021 Sep;137:176-181. doi: 10.1016/j.jclinepi.2021.04.004. Epub 2021 Apr 21.
The Bland and Altman limits of agreement (LoA) method is almost universally used to compare two measurement methods, when the outcome is continuous. The method relies on strong statistical assumptions, which are unlikely to hold in practice. Given the popularity of this simple method, it is timely to explain when it can be safely used and when it should not be used.
Based on a small sample of simulated data where the truth is known, we illustrate what happens when the LoA method is used and the underlying assumptions are violated.
When each measurement method has a different precision or the systematic difference between the two methods is not constant, the LoA method should not be used. For this setting, we refer to an alternative unbiased statistical method, which comes at the cost of having to gather repeated measurements by at least one of the two measurement methods.
The LoA method is valid under very restrictive conditions and when these conditions do not hold the only way out is to gather repeated measurements by at least one of the two measurement methods and use an alternative existing statistical methodology.
当结果为连续时， Bland 和 Altman 一致性界限（LoA）方法几乎被普遍用于比较两种测量方法。该方法依赖于强烈的统计假设，而这些假设在实践中不太可能成立。鉴于这种简单方法的普及，及时解释何时可以安全使用，何时不应使用该方法是很有必要的。
基于一个已知真相的小样本模拟数据，我们说明了当使用 LoA 方法且违反了基本假设时会发生什么情况。
当每种测量方法具有不同的精度或两种方法之间的系统差异不恒定时，不应使用 LoA 方法。对于这种设置，我们参考一种替代的无偏统计方法，但其代价是至少需要通过两种测量方法之一来收集重复测量。
LoA 方法在非常严格的条件下有效，并且当这些条件不成立时，唯一的出路是通过至少一种测量方法收集重复测量，并使用现有的替代统计方法。
