Conrad Daniela, Wilker Sarah, Pfeiffer Anett, Lingenfelder Birke, Ebalu Tracie, Lanzinger Hartmut, Elbert Thomas, Kolassa Iris-Tatjana, Kolassa Stephan
Clinical Psychology and Neuropsychology, Department of Psychology, University of Konstanz, Konstanz, Germany.
Clinical & Biological Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany.
Eur J Psychotraumatol. 2017 Jul 6;8(1):1344079. doi: 10.1080/20008198.2017.1344079. eCollection 2017.
: The likelihood of developing Posttraumatic Stress Disorder (PTSD) depends on the interaction of individual risk factors and cumulative traumatic experiences. Hence, the identification of individual susceptibility factors warrants precise quantification of trauma exposure. Previous research indicated that some traumatic events may have more severe influences on mental health than others; thus, the assessment of traumatic load may be improved by weighting event list items rather than calculating the simple sum score. : We compared two statistical methods, Random Forests using Conditional Interference (RF-CI) and Least Absolute Shrinkage and Selection Operator (LASSO), based on their ability to rank traumatic experiences according to their importance for predicting lifetime PTSD. : Statistical models were initially fitted in a sample of = 441 survivors of the Northern Ugandan rebel war. The ability to correctly predict lifetime PTSD was then tested in an independent sample of = 211, and subsequently compared with predictions by the simple sum score of different traumatic event types experienced. : Results indicate that RF-CI and LASSO allow for a ranking of traumatic events according to their predictive importance for lifetime PTSD. Moreover, RF-CI showed slightly better prediction accuracy than the simple sum score, followed by LASSO when comparing prediction results in the validation sample. : Given the expense in time and calculation effort by RF-CI and LASSO, and the relatively low increase in prediction accuracy by RF-CI, we recommend using the simple sum score to measure the environmental factor traumatic load, e.g., in analyses of gene × environment interactions.
创伤后应激障碍(PTSD)的发病可能性取决于个体风险因素与累积创伤经历的相互作用。因此,识别个体易感性因素需要对创伤暴露进行精确量化。先前的研究表明,某些创伤事件可能比其他事件对心理健康的影响更严重;因此,通过对事件列表项目进行加权而非计算简单的总分,可以改进对创伤负荷的评估。
我们比较了两种统计方法,即基于条件干预的随机森林(RF-CI)和最小绝对收缩和选择算子(LASSO),依据它们根据创伤经历对预测终生PTSD的重要性进行排序的能力。
统计模型最初在441名乌干达北部叛乱战争幸存者的样本中进行拟合。然后在一个211人的独立样本中测试正确预测终生PTSD的能力,随后将其与根据所经历的不同创伤事件类型的简单总分所做的预测进行比较。
结果表明,RF-CI和LASSO能够根据创伤事件对终生PTSD的预测重要性对其进行排序。此外,在验证样本中比较预测结果时,RF-CI的预测准确性略高于简单总分,其次是LASSO。
鉴于RF-CI和LASSO在时间和计算工作量方面的成本,以及RF-CI在预测准确性方面相对较低的提升,我们建议使用简单总分来衡量环境因素创伤负荷,例如在基因×环境相互作用的分析中。