统计工具的选择去除离群值会导致健康人群中分析物参考区间发生显著变化。
Choice of Statistical Tools for Outlier Removal Causes Substantial Changes in Analyte Reference Intervals in Healthy Populations.
机构信息
Australian National University Medical School, Garran, ACT, Australia.
ACT Pathology, Canberra Hospital, Garran, ACT, Australia.
出版信息
Clin Chem. 2020 Dec 1;66(12):1558-1561. doi: 10.1093/clinchem/hvaa208.
BACKGROUND
Reference intervals are an important aid in medical practice as they provide clinicians a guide as to whether a patient is healthy or diseased.Outlier results in population studies are removed by any of a variety of statistical measures. We have compared several methods of outlier removal and applied them to a large body of analytes from a large population of healthy persons.
METHODS
We used the outlier exclusion criteria of Reed-Dixon and Tukey and calculated reference intervals using nonparametric and Harrell-Davis statistical methods and applied them to a total of 36 different analytes.
RESULTS
Nine of 36 analytes had a greater than 20% difference in the upper reference limit, and for some the difference was 100% or more.
CONCLUSIONS
For some analytes, great importance is attached to the reference interval. We have shown that different statistical methods for outlier removal can cause large changes to reported reference intervals. So that population studies can be readily compared, common statistical methods should be used for outlier removal.
背景
参考区间是医学实践中的一个重要辅助手段，因为它为临床医生提供了一个指南，说明患者是否健康或患病。通过各种统计措施可以去除人群研究中的异常值结果。我们比较了几种异常值去除方法，并将其应用于大量来自健康人群的分析物。
方法
我们使用 Reed-Dixon 和 Tukey 的异常值排除标准，并使用非参数和 Harrell-Davis 统计方法计算参考区间，并将其应用于总共 36 种不同的分析物。
结果
36 种分析物中有 9 种的上限参考限值差异超过 20%，对于某些分析物，差异甚至达到 100%或更高。
结论
对于某些分析物，参考区间非常重要。我们已经表明，异常值去除的不同统计方法可能会导致报告的参考区间发生很大变化。为了便于比较人群研究，应使用通用的统计方法来去除异常值。
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