一种用于具有多项结果的组内kappa统计量的非迭代置信区间估计程序。

A non-iterative confidence interval estimating procedure for the intraclass kappa statistic with multinomial outcomes.

作者信息

Zou Guangyong, Klar Neil

机构信息

Robarts Clinical Trials, Robarts Research Institute, 100 Perth Drive, London, Ontario, Canada N6A 5K8.

出版信息

Biom J. 2005 Oct;47(5):682-90. doi: 10.1002/bimj.200310154.

DOI:10.1002/bimj.200310154

PMID:16385909

Abstract

We obtain the asymptotic sample variance of the intraclass kappa statistic for multinomial outcome data. A modified Wald type procedure based on this theory is then used for confidence interval construction. The results of a simulation study show that the proposed non-iterative approach performs very well in terms of confidence interval coverage and width for samples as small as 50. The procedure is illustrated with two examples from previously published medical studies.

摘要

我们获得了多项结果数据的组内kappa统计量的渐近样本方差。基于该理论的一种改进的Wald型程序随后用于构建置信区间。一项模拟研究结果表明，对于小至50的样本，所提出的非迭代方法在置信区间覆盖范围和宽度方面表现非常出色。该程序通过先前发表的两项医学研究中的例子进行说明。