心理信息
奇纳
潜在类模型
萧条(经济学)
背景(考古学)
斯科普斯
临床心理学
荟萃分析
心理学
系统回顾
梅德林
医学
精神科
心理干预
内科学
机器学习
生物
古生物学
经济
宏观经济学
生物化学
计算机科学
作者
Christine M. Ulbricht,Stavroula A. Chrysanthopoulou,Len Levin,Kate L. Lapane
标识
DOI:10.1016/j.psychres.2018.03.003
摘要
Depression is a significant public health problem but symptom remission is difficult to predict. This may be due to substantial heterogeneity underlying the disorder. Latent class analysis (LCA) is often used to elucidate clinically relevant depression subtypes but whether or not consistent subtypes emerge is unclear. We sought to critically examine the implementation and reporting of LCA in this context by performing a systematic review to identify articles detailing the use of LCA to explore subtypes of depression among samples of adults endorsing depression symptoms. PubMed, PsycINFO, CINAHL, Scopus, and Google Scholar were searched to identify eligible articles indexed prior to January 2016. Twenty-four articles reporting 28 LCA models were eligible for inclusion. Sample characteristics varied widely. The majority of articles used depression symptoms as the observed indicators of the latent depression subtypes. Details regarding model fit and selection were often lacking. No consistent set of depression subtypes was identified across studies. Differences in how models were constructed might partially explain the conflicting results. Standards for using, interpreting, and reporting LCA models could improve our understanding of the LCA results. Incorporating dimensions of depression other than symptoms, such as functioning, may be helpful in determining depression subtypes.
科研通智能强力驱动
Strongly Powered by AbleSci AI