Decomposing the heterogeneity of depression at the person-, symptom-, and time-level: latent variable models versus multimode principal component analysis
Decomposing the heterogeneity of depression at the person-, symptom-, and time-level: latent variable models versus multimode principal component analysis
复制标题
DOI：
10.1186/s12874-015-0080-4
复制
发表时间：
2015-10-15
影响因子：
4
通讯作者：
de Jonge, Peter
中科院分区：
文献类型：
--
作者：
de Vos, Stijn;Wardenaar, Klaas J.;de Jonge, Peter
背景：抑郁症等精神病理学概念的异质性阻碍了研究和临床实践的进展。潜变量模型（LVM）已被广泛用于通过识别更同质的因素或子组来减少这个问题。然而，异质性存在于多个层面（人、症状、时间），LVM 无法同时捕获所有这些层面及其相互作用，这导致模型不完整。我们的目标是简要回顾抑郁症研究中最广泛使用的 LVM，说明它们在实际数据中的用途和不兼容性，并考虑另一种统计方法，即多模式主成分分析 (MPCA)。 方法：我们将 LVM 应用于 147 名患者的数据，这些患者在 9 个时间点填写了抑郁症状学快速清单 (QIDS)。评估了结果的兼容性以及 LVM 捕获数据异质性的适用性。或者，使用 MPCA 在人、症状和时间水平上同时分解抑郁症，并研究这些水平之间的相互作用。结果：QIDS 数据可以在人水平（2 个类别）、症状水平（2 个因素）和时间水平（2 个轨迹类别）上进行分解。然而，这些结果无法集成到单个模型中。相反，MPCA 允许在人（3 个分量）、症状（2 个分量）和时间水平（2 个分量）上分解数据，并调查这些分量的相互作用。结论：在尝试定义人、症状和时间水平上抑郁症异质性的集成模型时，传统 LVM 的用途有限。 MPCA 等更综合的统计技术可用于解决这些相对复杂的数据模式，并可用于未来识别基于经验的抑郁症亚型/表型的尝试。
Background: Heterogeneity of psychopathological concepts such as depression hampers progress in research and clinical practice. Latent Variable Models (LVMs) have been widely used to reduce this problem by identification of more homogeneous factors or subgroups. However, heterogeneity exists at multiple levels (persons, symptoms, time) and LVMs cannot capture all these levels and their interactions simultaneously, which leads to incomplete models. Our objective is to briefly review the most widely used LVMs in depression research, illustrating their use and incompatibility in real data, and to consider an alternative, statistical approach, namely multimode principal component analysis (MPCA).Methods: We applied LVMs to data from 147 patients, who filled out the Quick Inventory of Depressive Symptomatology (QIDS) at 9 time points. Compatibility of the results and suitability of the LVMs to capture the heterogeneity of the data were evaluated. Alternatively, MPCA was used to simultaneously decompose depression on the person-, symptom- and time-level and to investigate the interactions between these levels.Results: QIDS-data could be decomposed on the person- level (2 classes), symptom- level (2 factors) and time-level (2 trajectory-classes). However, these results could not be integrated into a single model. Instead, MPCA allowed for decomposition of the data at the person- (3 components), symptom- (2 components) and time-level (2 components) and for the investigation of these components' interactions.Conclusions: Traditional LVMs have limited use when trying to define an integrated model of depression heterogeneity at the person, symptom and time level. More integrative statistical techniques such as MPCA can be used to address these relatively complex data patterns and could be used in future attempts to identify empirically-based subtypes/phenotypes of depression.