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Sequential rank agreement methods for comparison of ranked lists.
Claus Thorn Ekstrøm1, Thomas Alexander Gerds1, Andreas Kryger Jensen1
1Biostatistics, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5 B, DK-1014 Copenhagen K, Denmark.
Biostatistics (Oxford, England)
|June 6, 2018
Summary
We introduce a new sequential rank agreement measure for comparing multiple ordered lists. This method provides intuitive insights into agreement across lists, even with missing data, aiding statistical analysis in fields like genetics and cancer research.
Area of Science:
- Applied statistics
- Bioinformatics
- Biostatistics
Background:
- Comparing rankings is common in statistics, used for variable association, risk prediction, and gene expression analysis.
- Existing methods may not fully capture agreement across multiple or incomplete ranked lists.
Purpose of the Study:
- To propose a novel sequential rank agreement measure for quantifying agreement among two or more ordered lists.
- To provide a flexible and interpretable tool applicable to various datasets, including those with missing information.
Main Methods:
- Developed a sequential rank agreement measure.
- Analytical evaluation and graphical comparison to permutation-based reference sets for assessing agreement changes.
- Applied the measure to gene rankings and ovarian cancer classification data.
Main Results:
- The proposed measure offers intuitive interpretation and handles incomplete lists.
- Demonstrated utility in gene ranking analysis.
- Assessed within- and between-agreement of classification methods in ovarian cancer studies.
Conclusions:
- The sequential rank agreement measure is a valuable tool for comparing multiple ordered lists in applied statistics.
- It enhances understanding of agreement patterns in complex datasets, such as those in genetic and clinical research.
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