[1]张竞文,许军,安胜利.基于AC1系数的一致性评价方法[J].南方医科大学学报,2018,(04):455.
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基于AC1系数的一致性评价方法()
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《南方医科大学学报》[ISSN:/CN:]

卷:
期数:
2018年04期
页码:
455
栏目:
出版日期:
2018-04-30

文章信息/Info

Title:
A new method for agreement evaluation based on AC1
作者:
张竞文许军安胜利
关键词:
一致性kappa系数AC1系数分类变量
Keywords:
agreement Kappa coefficient AC1 coefficient categorical variables
摘要:
在医学研究中,目前常用的对不同测量者或测量方法的一致性评价方法有各自的限制条件,最为熟知的是kappa悖论,为 了克服这些缺陷并获得更高的准确性,本文基于AC1系数理论基础并通过探究偶然一致性和阳性事件率对整体一致性的影响, 提出了一种新的一致性评价方法一致性估计系数(CEA),并通过模拟及实例对比了kappa、AC1、CEA系数的准确性和稳定性。 本研究为二分类结局的一致性评价提供了一种稳定、可靠的方法选择。
Abstract:
Medical studies use various methods for assessing agreement among different raters or measurement methods. Many of these coefficients have limitations, and among them the paradoxes of kappa are the best known. To achieve a higher accuracy and reliability, we propose an alternative statistic method based on AC1, known as CEA, which adjusts the chance agreement. We explored the influences of the prevalence rate and chance agreement probability on the total agreement and compared the accuracy and stability of kappa, AC1 and CEA coefficient through simulations and real data analysis. The proposed method offers a stable and reliable option for assessing agreement of binary data.
更新日期/Last Update: 1900-01-01