分层评价设计中ac1反应率的同质性检验和样本量。

IF 1.2 4区 数学
Jingwei Jia, Yuanbo Liu, Jikai Yang, Zhiming Li
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引用次数: 0

摘要

Gwet的一阶一致系数(ac1)被广泛用于评价评价者之间的一致性。考虑到评分者之间存在一定的关系,本文的目的是在分层设计中检验反应率的相等性和修正AC 1的两个评分者之间的依赖关系,并估计给定显著性水平下的样本量。首先建立概率模型,然后对未知参数进行估计。进一步,我们探讨了这些AC 1在渐近方法下的同质性检验,如似然比、分数和wald型统计量。在数值模拟中,统计性能是根据I型错误率(TIEs)和功率来研究的,同时在给定功率下找到合适的样本量。结果表明,wald型统计量具有鲁棒性和令人满意的功率,适用于大样本(n≥50)。在相同的功率下,当岩层数较大时,wald型试验的样本量较小。功率越高,所需的样本量越大。最后,给出了两个实例来说明这些方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Homogeneity test and sample size of response rates for AC 1 in a stratified evaluation design.

Gwet's first-order agreement coefficient (AC 1) is widely used to evaluate the consistency between raters. Considering the existence of a certain relationship between the raters, the paper aims to test the equality of response rates and the dependency between two raters of modified AC 1's in a stratified design and estimates the sample size for a given significance level. We first establish a probability model and then estimate the unknown parameters. Further, we explore the homogeneity test of these AC 1's under the asymptotic method, such as likelihood ratio, score, and Wald-type statistics. In numerical simulation, the performance of statistics is investigated in terms of type I error rates (TIEs) and power while finding a suitable sample size under a given power. The results show that the Wald-type statistic has robust TIEs and satisfactory power and is suitable for large samples (n≥50). Under the same power, the sample size of the Wald-type test is smaller when the number of strata is large. The higher the power, the larger the required sample size. Finally, two real examples are given to illustrate these methods.

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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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