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Differences in Connection Strength between Mental Symptoms Might Be Explained by Differences in Variance: Reanalysis of Network Data Did Not Confirm Staging

机译:心理症状之间的连接强度差异可能由方差差异来解释:网络数据的重新分析未确认分期

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摘要

BACKGROUND: The network approach to psychopathology conceives mental disorders as sets of symptoms causally impacting on each other. The strengths of the connections between symptoms are key elements in the description of those symptom networks. Typically, the connections are analysed as linear associations (i.e., correlations or regression coefficients). However, there is insufficient awareness of the fact that differences in variance may account for differences in connection strength. Differences in variance frequently occur when subgroups are based on skewed data. An illustrative example is a study published in PLoS One (2013;8(3):e59559) that aimed to test the hypothesis that the development of psychopathology through "staging" was characterized by increasing connection strength between mental states. Three mental states (negative affect, positive affect, and paranoia) were studied in severity subgroups of a general population sample. The connection strength was found to increase with increasing severity in six of nine models. However, the method used (linear mixed modelling) is not suitable for skewed data. METHODS: We reanalysed the data using inverse Gaussian generalized linear mixed modelling, a method suited for positively skewed data (such as symptoms in the general population). RESULTS: The distribution of positive affect was normal, but the distributions of negative affect and paranoia were heavily skewed. The variance of the skewed variables increased with increasing severity. Reanalysis of the data did not confirm increasing connection strength, except for one of nine models. CONCLUSIONS: Reanalysis of the data did not provide convincing evidence in support of staging as characterized by increasing connection strength between mental states. Network researchers should be aware that differences in connection strength between symptoms may be caused by differences in variances, in which case they should not be interpreted as differences in impact of one symptom on another symptom.
机译:背景:心理病理学的网络方法将精神障碍视为因果关系相互影响的一系列症状。症状之间联系的强度是描述这些症状网络的关键要素。通常,将连接分析为线性关联(即相关性或回归系数)。但是,人们对差异的差异可能会解释连接强度差异这一事实的认识不足。当子组基于偏斜数据时,经常会出现方差差异。一个说明性的例子是发表在《公共科学图书馆·世界》(2013; 8(3):e59559)上的一项研究,该研究旨在检验以下假设:通过“分期”使精神病理学发展以精神状态之间的联系强度增加为特征。在一般人群样本的严重程度亚组中研究了三种精神状态(负面影响,正面影响和偏执狂)。在九个模型中的六个中,发现连接强度随严重程度的增加而增加。但是,所使用的方法(线性混合建模)不适用于偏斜的数据。方法:我们使用反高斯广义线性混合建模对数据进行了重新分析,该方法适用于正偏数据(例如一般人群中的症状)。结果:正面影响的分布是正常的,但是负面影响和偏执的分布严重偏斜。偏斜变量的方差随严重程度的增加而增加。对数据的重新分析并没有确认连接强度的增加,只有九个模型之一除外。结论:数据的重新分析没有提供令人信服的证据来支持分期,其特征是精神状态之间的联系强度增加。网络研究人员应意识到,症状之间的连接强度差异可能是由差异差异引起的,在这种情况下,不应将它们解释为一种症状对另一种症状的影响差异。

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