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Modeling and visualizing two-way contingency tables using compositional data analysis: A case-study on individual
Marina Vives-Mestres1,2, Amparo Casanova2
1Department of Computer Science, Applied Mathematics and Statistics, Universitat de Girona, Girona, Spain.
Statistics in Medicine
|October 28, 2020
Summary
Compositional data (CoDa) methods offer novel ways to analyze 2x2 contingency tables. This approach effectively visualizes and models relationships in medical studies, enhancing interpretation of self-prediction data.
Area of Science:
- Statistics
- Data Analysis
- Medical Research
Background:
- Two-way contingency tables are crucial for assessing relationships between discrete variables in fields like medicine.
- Existing methods may not fully capture the nuances of relationships within 2x2 tables, especially in repeated measures designs.
Purpose of the Study:
- To introduce and demonstrate the application of compositional data (CoDa) methods for analyzing and visualizing 2x2 contingency tables.
- To illustrate the utility of CoDa in a single-subject repeated measurements design using a real-world migraine prediction study.
Main Methods:
- Utilized quaternary diagrams for visualizing 2x2 tables.
- Employed logratios to represent the strength and direction of variable relationships.
- Applied simplicial regression modeling to analyze the contingency tables.
Main Results:
- Successfully visualized and interpreted individual prediction abilities using CoDa techniques.
- Demonstrated the modeling of self-prediction accuracy concerning demographic and disease-related factors.
- Confirmed the effectiveness of CoDa in dissecting the components of 2x2 tables.
Conclusions:
- Compositional data analysis provides a powerful framework for the visualization and interpretation of 2x2 contingency tables.
- The proposed CoDa methodology enhances the understanding of relationships and predictive abilities in complex datasets, such as medical self-prediction studies.
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