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基于相关配对数据的比例比的渐近置信区间构建

Asymptotic confidence interval construction for proportion ratio based on correlated paired data.

作者信息

Peng Xuan, Liu Chang, Liu Song, Ma Chang-Xing

机构信息

Department of Biostatistics, University at Buffalo, Buffalo, New York, USA.

Department of Mathematics, Southern University of Science and Technology, Shenzhen, Guangdong, P.R. China.

出版信息

J Biopharm Stat. 2019;29(6):1137-1152. doi: 10.1080/10543406.2019.1584629. Epub 2019 Mar 4.

Abstract

In ophthalmological and otolaryngology studies, measurements obtained from both organs (e.g., eyes or ears) of an individual are often highly correlated. Ignoring the intraclass correlation between paired measurements may yield biased inferences. In this article, four different confidence interval (CI) construction methods (maximum likelihood estimates based Wald-type CI, profile likelihood CI, asymptotic score CI and an existing method adjusted for correlated bilateral data) are applied to this type of correlated bilateral data to construct CI for proportion ratio, taking the intraclass correlation into consideration. The coverage probabilities and widths of the resulting CIs are compared with each other in a Monte Carlo simulation study to evaluate their performances. A real dataset from an ophthalmologic study is used to illustrate our methodology.

摘要

在眼科和耳鼻喉科研究中,从个体的两个器官(如眼睛或耳朵)获得的测量值通常高度相关。忽略配对测量之间的组内相关性可能会产生有偏差的推断。在本文中,四种不同的置信区间(CI)构建方法(基于最大似然估计的Wald型CI、轮廓似然CI、渐近得分CI以及一种针对相关双侧数据调整的现有方法)被应用于这类相关双侧数据,以构建比例比的CI,同时考虑组内相关性。在蒙特卡罗模拟研究中,将所得CI的覆盖概率和宽度相互比较,以评估它们的性能。使用一个眼科研究的真实数据集来说明我们的方法。

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