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用于比较测量者和测量方法的统计技术:批判性综述。

Statistical techniques for comparing measurers and methods of measurement: a critical review.

作者信息

Ludbrook John

机构信息

The University of Melbourne, Parkville, Victoria, Australia.

出版信息

Clin Exp Pharmacol Physiol. 2002 Jul;29(7):527-36. doi: 10.1046/j.1440-1681.2002.03686.x.

DOI:10.1046/j.1440-1681.2002.03686.x
PMID:12060093
Abstract
  1. Clinical and experimental pharmacologists and physiologists often wish to compare two methods of measurement, or two measurers. 2. Biostatisticians insist that what should be sought is not agreement between methods or measurers, but disagreement or bias. 3. If measurements have been made on a continuous scale, the main choice is between the Altman-Bland method of differences and least products regression analysis. It is argued that although the former is relatively simple to execute, it does not distinguish adequately between fixed and proportional bias. Least products regression analysis, although more difficult to execute, does achieve this goal. There is almost universal agreement among biostatisticians that the Pearson product-moment correlation coefficient (r) is valueless as a test for bias. 4. If measurements have been made on a categorical scale, unordered or ordered, the most popular method of analysis is to use the kappa statistic. If the categories are unordered, the unweighted kappa statistic (K) is appropriate. If the categories are ordered, as they are in most rating scales in clinical, psychological and epidemiological research, the weighted kappa statistic (K(w)) is preferable. But K(w) corresponds to the intraclass correlation coefficient, which, like r for continuous variables, is incapable of detecting bias. Simple techniques for detecting bias in the case of ordered categorical variables are described and commended to investigators.
摘要
  1. 临床和实验药理学家与生理学家常常希望比较两种测量方法或两个测量者。2. 生物统计学家坚持认为,应该寻求的不是方法或测量者之间的一致性,而是不一致性或偏差。3. 如果测量是在连续尺度上进行的,主要的选择是在奥特曼-布兰德差异法和最小乘积回归分析之间。有人认为,虽然前者执行起来相对简单,但它不能充分区分固定偏差和比例偏差。最小乘积回归分析虽然执行起来更困难,但确实能实现这一目标。生物统计学家几乎普遍认为,皮尔逊积矩相关系数(r)作为偏差检验毫无价值。4. 如果测量是在分类尺度上进行的,无论是无序的还是有序的,最常用的分析方法是使用kappa统计量。如果类别是无序的,未加权的kappa统计量(K)是合适的。如果类别是有序的,就像临床、心理和流行病学研究中的大多数评分量表那样,加权的kappa统计量(K(w))更可取。但是K(w)对应于组内相关系数,它与连续变量的r一样,无法检测偏差。本文描述了在有序分类变量情况下检测偏差的简单技术,并推荐给研究人员。

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