Arch Suicide Res. 2020 Apr-Jun;24(2):218-235. doi: 10.1080/13811118.2019.1615018. Epub 2019 Jun 10.
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. Data were drawn from the National Longitudinal Study of Adolescent to Adult Health. Youth (n = 4,834, M = 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 indicated two classification tree solutions that maximized risk prediction, with 69.8%/85.7% sensitivity/specificity and 90.6%/70.9% sensitivity/specificity, respectively. 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.
本研究应用分类树分析在一个大型青少年社区样本中前瞻性地识别自杀未遂者，以展示这种方法在风险识别方面的优势和局限性。数据来自国家青少年至成年健康纵向研究。青年（n=4834，M=16.15，SD=1.63，52.3%为女性，63.7%为白人）在第 1 波完成了家庭访谈，并在 12 个月后的第 2 波完成了自杀尝试的测量。结果表明，有两种分类树解决方案可以最大限度地提高风险预测，其灵敏度/特异性分别为 69.8%/85.7%和 90.6%/70.9%。分类树为识别有自杀未遂风险的个体提供了一种技术。分类树产生易于实施的决策规则和定制的筛选方法，可以根据特定组织的目标进行调整。
