期刊
DEPRESSION AND ANXIETY
卷 36, 期 9, 页码 790-800出版社
WILEY
DOI: 10.1002/da.22942
关键词
diagnosis; military personnel; psychological tests; psychometrics; trauma- and stressor-related disorders
Background Although several short-forms of the posttraumatic stress disorder (PTSD) Checklist (PCL) exist, all were developed using heuristic methods. This report presents the results of analyses designed to create an optimal short-form PCL for DSM-5 (PCL-5) using both machine learning and conventional scale development methods. Methods The short-form scales were developed using independent datasets collected by the Army Study to Assess Risk and Resilience among Service members. We began by using a training dataset (n = 8,917) to fit short-form scales with between 1 and 8 items using different statistical methods (exploratory factor analysis, stepwise logistic regression, and a new machine learning method to find an optimal integer-scored short-form scale) to predict dichotomous PTSD diagnoses determined using the full PCL-5. A smaller subset of best short-form scales was then evaluated in an independent validation sample (n = 11,728) to select one optimal short-form scale based on multiple operating characteristics (area under curve [AUC], calibration, sensitivity, specificity, net benefit). Results Inspection of AUCs in the training sample and replication in the validation sample led to a focus on 4-item integer-scored short-form scales selected with stepwise regression. Brier scores in the validation sample showed that a number of these scales had comparable calibration (0.015-0.032) and AUC (0.984-0.994), but that one had consistently highest net benefit across a plausible range of decision thresholds. Conclusions The recommended 4-item integer-scored short-form PCL-5 generates diagnoses that closely parallel those of the full PCL-5, making it well-suited for screening.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据