Intraclass Correlation (ICC) is a statistical measure used to assess the degree of similarity between observations within the same group or cluster, often applied in fields like biomedical research and machine learning.
Intraclass Correlation (ICC) is a widely used statistical method for quantifying the degree of similarity between observations within the same group or cluster. This measure is particularly relevant in biomedical research and machine learning, where data often exhibit hierarchical structures or are organized into clusters. ICC helps researchers understand the relationships between observations and identify potential patterns or trends within the data.
Recent research has focused on extending the applicability of ICC to various types of data, such as skewed distributions, count data, and ordered categorical data. For instance, the rank ICC has been proposed as an extension of Fisher's ICC to the rank scale, offering a more robust measure of similarity that is less sensitive to extreme values and skewed distributions. Additionally, researchers have developed methods for analyzing ICC in the context of complex data structures, such as multilevel models for count data and generalized linear models for correlated binary outcomes.
Some practical applications of ICC include assessing the reliability of biometric features, evaluating the test-retest reliability of brain connectivity matrices, and analyzing the local dynamic stability of gait in the context of fall risk assessment. In these cases, ICC has proven to be a valuable tool for understanding the underlying relationships between observations and informing the development of more effective interventions or treatments.
One company that has successfully applied ICC in their work is ν-net, which developed a deep learning approach for fully automated segmentation of right and left ventricular endocardium and epicardium in cardiac MRI images. By leveraging ICC, the company was able to achieve high-quality segmentation results and reliably determine biventricular mass and function parameters.
In conclusion, Intraclass Correlation (ICC) is a powerful statistical tool for understanding the relationships between observations within the same group or cluster. Its applications span a wide range of fields, including biomedical research and machine learning, and its continued development promises to unlock new insights and opportunities for researchers and practitioners alike.
Intraclass Correlation (ICC)
Intraclass Correlation (ICC) Further Reading
1.Rank Intraclass Correlation for Clustered Data http://arxiv.org/abs/2303.04880v1 Shengxin Tu, Chun Li, Donglin Zeng, Bryan E. Shepherd2.Marginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes http://arxiv.org/abs/2101.00484v1 Fan Li, Hengshi Yu, Paul J. Rathouz, Elizabeth L. Turner, John S. Preisser3.Generalized reliability based on distances http://arxiv.org/abs/1912.07137v2 Meng Xu, Philip T. Reiss, Ivor Cribben4.Method to Assess the Temporal Persistence of Potential Biometric Features: Application to Oculomotor, and Gait-Related Databases http://arxiv.org/abs/1609.03948v1 Lee Friedman, Ioannis Rigas, Mark S. Nixon, Oleg V. Komogortsev5.Variance partitioning in multilevel models for count data http://arxiv.org/abs/1911.06888v2 George Leckie, William Browne, Harvey Goldstein, Juan Merlo, Peter Austin6.$ν$-net: Deep Learning for Generalized Biventricular Cardiac Mass and Function Parameters http://arxiv.org/abs/1706.04397v1 Hinrich B Winther, Christian Hundt, Bertil Schmidt, Christoph Czerner, Johann Bauersachs, Frank Wacker, Jens Vogel-Claussen7.Power analysis for cluster randomized trials with continuous co-primary endpoints http://arxiv.org/abs/2112.01981v2 Siyun Yang, Mirjam Moerbeek, Monica Taljaard, Fan Li8.Local dynamic stability of treadmill walking: intrasession and week-to-week repeatability http://arxiv.org/abs/1310.4946v1 Fabienne Reynard, Philippe Terrier9.To what extent does not wearing shoes affect the local dynamic stability of the gait? Effect size and intra-session repeatability http://arxiv.org/abs/1212.5510v4 Philippe Terrier, Fabienne Reynard10.GANDA: A deep generative adversarial network predicts the spatial distribution of nanoparticles in tumor pixelly http://arxiv.org/abs/2012.12561v2 Jiulou Zhang, Yuxia Tang, Shouju WangIntraclass Correlation (ICC) Frequently Asked Questions
What does the intraclass correlation ICC represent?
Intraclass Correlation (ICC) represents a statistical measure that quantifies the degree of similarity between observations within the same group or cluster. It is commonly used in fields like biomedical research and machine learning, where data often exhibit hierarchical structures or are organized into clusters. ICC helps researchers understand the relationships between observations and identify potential patterns or trends within the data.
What does the ICC tell us?
The ICC tells us how similar the observations within a group or cluster are to each other. A high ICC value indicates that the observations within a group are more similar to each other than to observations from different groups. This information can be useful for understanding the underlying structure of the data, identifying potential patterns, and informing the development of more effective interventions or treatments.
What is the Intracluster correlation coefficient ICC?
The Intracluster correlation coefficient (ICC) is a statistical measure that assesses the degree of similarity between observations within the same group or cluster. It is particularly relevant in fields where data often exhibit hierarchical structures or are organized into clusters, such as biomedical research and machine learning.
What does ICC measure?
ICC measures the degree of similarity between observations within the same group or cluster. It helps researchers understand the relationships between observations and identify potential patterns or trends within the data. ICC can be applied to various types of data, such as skewed distributions, count data, and ordered categorical data.
How is ICC calculated?
ICC is calculated using a ratio of the variance between groups to the total variance, which includes both the variance between groups and the variance within groups. There are several different forms of ICC, depending on the specific research design and the assumptions made about the data. Some common forms include ICC(1), ICC(2), and ICC(3), each with its own formula and interpretation.
Why is ICC important in machine learning?
In machine learning, ICC is important because it helps researchers understand the relationships between observations within the same group or cluster. This information can be useful for identifying potential patterns, informing the development of more effective models, and evaluating the performance of algorithms. Additionally, ICC can be used to assess the reliability of features or predictions, which is crucial for ensuring the robustness and generalizability of machine learning models.
How does ICC differ from Pearson's correlation coefficient?
While both ICC and Pearson's correlation coefficient are measures of association, they serve different purposes. ICC is used to assess the degree of similarity between observations within the same group or cluster, whereas Pearson's correlation coefficient measures the linear relationship between two continuous variables. In other words, ICC focuses on the similarity within groups, while Pearson's correlation focuses on the relationship between two variables across all observations.
Can ICC be used for categorical data?
Yes, ICC can be used for categorical data, particularly ordered categorical data. Researchers have developed extensions of ICC for various types of data, including count data and ordered categorical data. For instance, the rank ICC has been proposed as an extension of Fisher's ICC to the rank scale, offering a more robust measure of similarity that is less sensitive to extreme values and skewed distributions.
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