Numero: a statistical framework to define multivariable subgroups in complex population-based datasets

Song Gao, Stefan Mutter, Aaron Casey, Ville-Petteri Makinen

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Large-scale epidemiological and population data provide opportunities to identify subgroups of people who are at risk of disease or exposed to adverse environments. Clustering algorithms are popular data-driven tools to identify these subgroups; however, relying exclusively on algorithms may not produce the best results if the dataset does not have a clustered structure. For this reason, we propose a framework (the R-library Numero) that combines the self-organizing map algorithm, permutation analysis for statistical evidence and a final expert-driven subgrouping step. We used Numero to define subgroups in two examples without an obvious clustering structure: a biomedical dataset of kidney disease and another dataset of community-level socioeconomic indicators. We benchmarked the Numero subgroupings against popular clustering algorithms (principal components, K-means and hierarchical clustering). The Numero subgroupings were more intuitive and easier to interpret without losing mathematical quality. Therefore, we expect Numero to be useful for exploratory analyses of population-based epidemiological datasets.

Original languageEnglish
Pages (from-to)369-374
Number of pages6
JournalInternational journal of epidemiology
Issue number2
Publication statusPublished - 1 Jan 2019


  • Data-driven subgrouping
  • Multivariable statistics
  • Population data
  • Self-organizing map

ASJC Scopus subject areas

  • Epidemiology

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