精神分裂症的症状结构：潜在变量建模与网络分析的启示。
Symptom Structure in Schizophrenia: Implications of Latent Variable Modeling vs Network Analysis.
机构信息
Desert Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
出版信息
Schizophr Bull. 2022 May 7;48(3):538-543. doi: 10.1093/schbul/sbac020.
The structure of schizophrenia symptoms has a substantial impact on the development of pharmacological and psychosocial interventions. Typically, reflective latent variable models (eg, confirmatory factor analysis) or formative latent variable models (eg, principal component analysis) have been used to examine the structure of schizophrenia symptoms. More recently, network analysis is appearing as a method to examine symptom structure. However, latent variable modeling and network analysis results can lead to different inferences about the nature of symptoms. Given the critical role of correctly identifying symptom structure in schizophrenia treatment and research, we present an introduction to latent variable modeling and network analysis, along with their distinctions and implications for examining the structure of schizophrenia symptoms. We also provide a simulation demonstration highlighting the statistical equivalence between these models and the subsequent importance of an a priori rationale that should help guide model selection.
精神分裂症症状的结构对药理学和心理社会干预的发展有重大影响。通常，反映潜变量模型（例如，验证性因子分析）或形成潜变量模型（例如，主成分分析）已被用于检查精神分裂症症状的结构。最近，网络分析作为一种检查症状结构的方法出现。然而，潜变量建模和网络分析的结果可能导致对症状性质的不同推断。鉴于正确识别精神分裂症治疗和研究中症状结构的关键作用，我们介绍了潜变量建模和网络分析，以及它们的区别及其对检查精神分裂症症状结构的意义。我们还提供了一个模拟演示，突出了这些模型之间的统计学等效性，以及应该有助于指导模型选择的先验原理的重要性。
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