Conservative confidence intervals for the intraclass correlation coefficient for clustered binary data

© 2021 Informa UK Limited, trading as Taylor & Francis Group.

Bibliographische Detailangaben
Veröffentlicht in:Journal of applied statistics. - 1991. - 49(2022), 10 vom: 07., Seite 2535-2549
1. Verfasser: Shan, Guogen (VerfasserIn)
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2022
Zugriff auf das übergeordnete Werk:Journal of applied statistics
Schlagworte:Journal Article Clustered binary data confidence interval importance sampling intraclass correlation coefficient profile confidence limit
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520 |a Asymptotic approaches are traditionally used to calculate confidence intervals for intraclass correlation coefficient in a clustered binary study. When sample size is small to medium, or correlation or response rate is near the boundary, asymptotic intervals often do not have satisfactory performance with regard to coverage. We propose using the importance sampling method to construct the profile confidence limits for the intraclass correlation coefficient. Importance sampling is a simulation based approach to reduce the variance of the estimated parameter. Four existing asymptotic limits are used as statistical quantities for sample space ordering in the importance sampling method. Simulation studies are performed to evaluate the performance of the proposed accurate intervals with regard to coverage and interval width. Simulation results indicate that the accurate intervals based on the asymptotic limits by Fleiss and Cuzick generally have shorter width than others in many cases, while the accurate intervals based on Zou and Donner asymptotic limits outperform others when correlation and response rate are close to their boundaries 
650 4 |a Journal Article 
650 4 |a Clustered binary data 
650 4 |a confidence interval 
650 4 |a importance sampling 
650 4 |a intraclass correlation coefficient 
650 4 |a profile confidence limit 
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