Francq Bernard G, Berger Marion, Boachie Charles
Technical R&D - CMC Statistical Sciences, GSK, Rixensart, Belgium.
Biostatistics and Programming, Non Clinical Biostatistics, Sanofi, Montpellier, France.
Stat Med. 2020 Dec 10;39(28):4334-4349. doi: 10.1002/sim.8709. Epub 2020 Sep 23.
The well-known agreement interval by Bland and Altman is extensively applied in method comparison studies. Two clinical measurement methods are considered interchangeable if their differences are not clinically significant. The agreement interval is commonly applied to assess the spread of the differences. However, this interval is approximate (too narrow) and several authors propose calculating a confidence interval around each bound. This article demonstrates that this approach is misleading, awkward, and confusing. On the other hand, tolerance intervals are exact and can include a confidence level if needed. Tolerance intervals are also easier to calculate and to interpret. Real data sets are used to illustrate the tolerance intervals with the R package BivRegBLS under normal or log-normal assumptions. Furthermore, it is also explained how to assess the coverage probabilities of the tolerance intervals with simulations.
布兰德和奥特曼提出的著名一致性区间在方法比较研究中得到了广泛应用。如果两种临床测量方法的差异在临床上不显著，那么这两种方法就被认为是可互换的。一致性区间通常用于评估差异的分布范围。然而，这个区间是近似的（太窄），一些作者建议在每个边界周围计算一个置信区间。本文表明，这种方法具有误导性、笨拙且令人困惑。另一方面，容忍区间是精确的，并且如果需要可以包含一个置信水平。容忍区间也更容易计算和解释。本文使用实际数据集，在正态或对数正态假设下，用R包BivRegBLS来说明容忍区间。此外，还解释了如何通过模拟来评估容忍区间的覆盖概率。
