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

ABSTRACT:

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, recei

SUBMITTER: Mitchell SM 

PROVIDER: S-EPMC7995491 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature