使用评分不确定性数据的新型一致性统计。

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Jarcy Zee, Laura Mariani, Laura Barisoni, Parag Mahajan, Brenda Gillespie
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引用次数: 0

摘要

现有的许多估计一致性的方法都是根据不同的假设,用偶然一致性的概率来调整观察到的一致性比例,从而纠正偶然一致性。病理学家对肾活检描述指标的评分表明,这些假设并不总是合适的。我们提出了一种新的一致性统计方法,该方法考虑了偶然一致性的经验概率,并通过收集有关每次评分的评分者不确定性的额外数据进行估算。我们还推导出了该统计量的标准误差估计值。模拟研究表明,在大多数情况下,我们提出的统计量在剔除偶然一致后,对一致概率的估计是无偏的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel agreement statistic using data on uncertainty in ratings.

Many existing methods for estimating agreement correct for chance agreement by adjusting the observed proportion agreement by the probability of chance agreement based on different assumptions. These assumptions may not always be appropriate, as demonstrated by pathologists' ratings of kidney biopsy descriptors. We propose a novel agreement statistic that accounts for the empirical probability of chance agreement, estimated by collecting additional data on rater uncertainty for each rating. A standard error estimator for the proposed statistic is derived. Simulation studies show that in most cases, our proposed statistic is unbiased in estimating the probability of agreement after removing chance agreement.

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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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