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

Area of Science:

• Psychiatry and Mental Health
• Biostatistics
• Data Science

Background:

• Categorical data analysis is crucial for suicide risk and prevention research focusing on discrete outcomes like suicide attempt status.
• Current statistical methods are often misinterpreted or not presented in a clinically tangible manner, hindering their application.
• Bridging the gap between research findings and clinical practice requires improved communication of statistical results.

Purpose of the Study:

• To address the misinterpretation and lack of clinical tangibility of categorical data analysis results in suicide research.
• To highlight the relevance and utility of categorical methods in suicide research and clinical assessment.
• To introduce basic machine learning concepts and their distinct utility in this field.

Main Methods:

• Review of relevant background concepts and pertinent issues in categorical data analysis for suicide research.
• Non-technical descriptions and tutorials for conveying statistical results, including logistic regression, ROC curves, AUC statistics, and clinical cutoff scores.
• Demonstration of converting logistic regression estimates into predicted probability values with R and Microsoft Excel examples.

Main Results:

• Comprehensive examples using simulated data to illustrate the interpretation of categorical analysis results.
• Identification of important considerations for conducting and interpreting these statistical analyses.
• Provision of appendices for generating publication-ready figures in R and Microsoft Excel.

Conclusions:

• Translating statistical estimates into practical, clinically tangible information is key to advancing suicide prevention.
• Enhanced understanding and application of categorical data analysis can improve clinical assessment of suicide risk.
• The study aims to narrow the divide between research and clinical practice through accessible statistical interpretation.