Using categorical data analyses in suicide research: Considering clinical utility and practicality

Sean M. Mitchell, Ian Cero, Andrew K. Littlefield, Sarah L. Brown

Research output: Contribution to journalArticlepeer-review


Objective: Categorical data analysis is relevant to suicide risk and prevention research that focuses on discrete outcomes (e.g., suicide attempt status). Unfortunately, results from these analyses are often misinterpreted and not presented in a clinically tangible manner. We aimed to address these issues and highlight the relevance and utility of categorical methods in suicide research and clinical assessment. Additionally, we introduce relevant basic machine learning methods concepts and address the distinct utility of the current methods. Method: We review relevant background concepts and pertinent issues with references to helpful resources. We also provide non-technical descriptions and tutorials of how to convey categorical statistical results (logistic regression, receiver operating characteristic [ROC] curves, area under the curve [AUC] statistics, clinical cutoff scores) for clinical context and more intuitive use. Results: We provide comprehensive examples, using simulated data, and interpret results. We also note important considerations for conducting and interpreting these analyses. We provide a walk-through demonstrating how to convert logistic regression estimates into predicted probability values, which is accompanied by Appendices demonstrating how to produce publication-ready figures in R and Microsoft Excel. Conclusion: Improving the translation of statistical estimates to practical, clinically tangible information may narrow the divide between research and clinical practice.

Original languageEnglish
Pages (from-to)76-87
Number of pages12
JournalSuicide and Life-Threatening Behavior
Issue number1
StatePublished - Feb 2021


  • Categorical methods
  • clinical utility
  • logistic regression
  • probability
  • tutorial


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