Citation

Hill RM, Oosterhoff B, Do C. Arch. Suicide Res. 2019; ePub(ePub): 1-18.

Copyright

(Copyright © 2019, International Academy of Suicide Research, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/13811118.2019.1615018

PMID

31079565

Abstract

OBJECTIVE: This study applies classification tree analysis to prospectively identify suicide attempters among a large adolescent community sample, to demonstrate the strengths and limitations of this approach for risk identification.

METHOD: Data were drawn from the National Longitudinal Study of Adolescent to Adult Health. Youth (n = 4,834, M_age=16.15, SD = 1.63, 52.3% female, 63.7% White) completed at-home interviews at Wave 1 and a measure of suicide attempts 12 months later, at Wave 2.

RESULTS: Results indicated two classification tree solutions that maximized risk prediction, with 69.8%/85.7% sensitivity/specificity and 90.6%/70.9% sensitivity/specificity, respectively.

CONCLUSION: Classification trees provide a technique for identification of individuals at-risk for suicide attempts. Classification trees produce easy-to-implement decision rules and tailored screening approaches that can be adapted to the goals of a particular organization.

Language: en

Keywords

adolescent; classification tree analysis; machine learning; suicide attempt