在重度抑郁症治疗期间检测突然好转：蒙特卡洛分析的警示
Detecting Sudden Gains during Treatment of Major Depressive Disorder: Cautions from a Monte Carlo Analysis.
作者信息
Vittengl Jeffrey R, Clark Lee Anna, Thase Michael E, Jarrett Robin B
机构信息
Department of Psychology, Truman State University.
Department of Psychology, University of Notre Dame.
出版信息
Curr Psychiatry Rev. 2015 Feb 1;11(1):19-31. doi: 10.2174/1573400510666140929195441.
Sudden gains are relatively large, quick, stable drops in symptom scores during treatment of depression that may (or may not) signal important therapeutic events. We review what is known and unknown currently about the prevalence, causes, and outcomes of sudden gains. We argue that valid identification of sudden gains (vs. random fluctuations in symptoms and gradual gains) is prerequisite to their understanding. In Monte Carlo simulations, three popular criterion sets showed inadequate power to detect sudden gains and many false positives due to (a) testing multiple intervals for sudden gains, (b) finite retest reliability of symptom measures, and (c) failure to account for gradual gains. Sudden gains in published clinical datasets appear similar in form and frequency to false positives in the simulations. We discuss the need to develop psychometrically sound methods to detect sudden gains and to differentiate sudden from random and gradual gains.
突然改善是指在抑郁症治疗期间症状评分相对较大、迅速且稳定的下降，这可能（也可能不）预示着重要的治疗事件。我们回顾了目前关于突然改善的患病率、成因及结果已知和未知的情况。我们认为，有效识别突然改善（相对于症状的随机波动和逐渐改善）是理解它们的先决条件。在蒙特卡洛模拟中，三种常用的标准集检测突然改善的能力不足，且由于（a）对突然改善的多个时间段进行检验、（b）症状测量的重测信度有限以及（c）未考虑逐渐改善，出现了许多假阳性结果。已发表临床数据集中的突然改善在形式和频率上与模拟中的假阳性结果相似。我们讨论了开发心理测量学上合理的方法来检测突然改善，并区分突然改善与随机和逐渐改善的必要性。
Zotero 插件