Beyond Point Estimates: Toward Proper Statistical Inferencing and Reporting of Intraclass Correlation Coefficients
Yufeng Liu, Xiangfei Hong, Shanbao Tong

TL;DR
This paper emphasizes the importance of proper statistical inference for ICCs in neuroimaging, highlighting common misreporting issues and providing guidelines and tools to improve reliability and validity of ICC-based conclusions.
Contribution
It offers practical guidelines, introduces an online tool for ICC inference, and reassesses recent neuroimaging studies to improve ICC reporting standards.
Findings
Most reviewed studies misreported ICCs or lacked valid statistical inference.
Only 2 out of 11 studies provided valid ICC inferences.
Misuse of ICCs can lead to unreliable scientific conclusions.
Abstract
Reporting test-retest reliability using the intraclass correlation coefficient (ICC) has received increasing attention due to the criticisms of poor transparency and replicability in neuroimaging research, as well as many other biomedical studies. Numerous studies have thus evaluated the reliability of their findings by comparing ICCs, however, they often failed to test statistical differences between ICCs or report confidence intervals. Relying solely on point estimates may preclude valid inference about population-level differences and compromise the reliability of conclusions. To address this issue, this study systematically reviewed the use of ICC in articles published in NeuroImage from 2022 to 2024, highlighting the prevalence of misreporting and misuse of ICCs. We further provide practical guidelines for conducting appropriate statistical inference on ICCs. For practitioners in…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFunctional Brain Connectivity Studies · Advanced Neuroimaging Techniques and Applications · Meta-analysis and systematic reviews
