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Cross-Modal Multivariate Pattern Analysis
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Numero: a statistical framework to define multivariable subgroups in complex population-based datasets.

Song Gao1, Stefan Mutter1, Aaron Casey1

  • 1Heart Health Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia.

International Journal of Epidemiology
|June 28, 2018
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Summary

The Numero framework combines algorithms and expert input for subgroup discovery in epidemiological data. It offers more intuitive and interpretable results than traditional clustering methods.

Keywords:
Multivariable statisticsdata-driven subgroupingpopulation dataself-organizing map

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Area of Science:

  • Epidemiology
  • Data Science
  • Bioinformatics

Background:

  • Large-scale epidemiological data can identify at-risk populations.
  • Clustering algorithms are common but may fail with non-clustered data.

Purpose of the Study:

  • To introduce the Numero R-library for robust subgroup identification.
  • To combine self-organizing maps, permutation analysis, and expert input.

Main Methods:

  • Developed the Numero R-library.
  • Applied Numero to kidney disease and socioeconomic datasets.
  • Benchmarked against principal components, K-means, and hierarchical clustering.

Main Results:

  • Numero identified intuitive and interpretable subgroups in datasets lacking clear clustering.
  • Performance was comparable to established clustering algorithms.

Conclusions:

  • Numero provides a valuable framework for exploratory analysis of population-based epidemiological data.
  • The approach enhances subgroup discovery when traditional methods fall short.