Quantifying the Reliability and Replicability of Psychopathology Network Characteristics
Quantifying the Reliability and Replicability of Psychopathology Network Characteristics
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DOI：
10.1080/00273171.2019.1616526
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发表时间：
2019-05-28
影响因子：
3.8
通讯作者：
Krueger, Robert F.
中科院分区：
文献类型：
--
作者：
Forbes, Miriam K.;Wright, Aidan G. C.;Krueger, Robert F.
成对马尔可夫随机场网络-包括高斯图形模型（GGM）和伊辛模型-已成为精神病理学网络分析的“最先进”方法。最近的研究集中在这些网络的可靠性和可复制性。在本研究中，我们比较了现有的一套方法，用于最大化和量化PMRF网络的稳定性和一致性（即，lasso正则化，加上R中的bootnet和NetworkComparisonTest包），具有一组用于直接比较文献中解释的详细网络特性的度量（例如，每个单独边缘的存在、不存在、符号和强度）。我们比较了GGM的抑郁和焦虑症状的两波数据从观察性研究（n = 403）和重新分析四个创伤后应激障碍GGM从最近的网络可复制性的研究。从表面上看，现有的一套方法表明，总体上网络边缘是稳定的，可解释的，并且在网络之间是一致的，但复制的直接指标表明情况并非如此（例如，在成对比较中，每个网络中39-49%的边是未复制的）。我们讨论这些明显矛盾的结果的原因（例如，依赖全球汇总统计数据，而不是审查文献中解释的详细特征），并得出结论认为，这里观察到的网络详细特征的可靠性有限，这在实践中可能很常见，但被当前的方法所忽视。鉴于可推广的结论对于其结果的实用性至关重要，可复制性差是我们对这些方法使用的关注的基础。
Pairwise Markov random field networks-including Gaussian graphical models (GGMs) and Ising models-have become the "state-of-the-art" method for psychopathology network analyses. Recent research has focused on the reliability and replicability of these networks. In the present study, we compared the existing suite of methods for maximizing and quantifying the stability and consistency of PMRF networks (i.e., lasso regularization, plus the bootnet and NetworkComparisonTest packages in R) with a set of metrics for directly comparing the detailed network characteristics interpreted in the literature (e.g., the presence, absence, sign, and strength of each individual edge). We compared GGMs of depression and anxiety symptoms in two waves of data from an observational study (n = 403) and reanalyzed four posttraumatic stress disorder GGMs from a recent study of network replicability. Taken on face value, the existing suite of methods indicated that overall the network edges were stable, interpretable, and consistent between networks, but the direct metrics of replication indicated that this was not the case (e.g., 39-49% of the edges in each network were unreplicated across the pairwise comparisons). We discuss reasons for these apparently contradictory results (e.g., relying on global summary statistics versus examining the detailed characteristics interpreted in the literature) and conclude that the limited reliability of the detailed characteristics of networks observed here is likely to be common in practice, but overlooked by current methods. Poor replicability underpins our concern surrounding the use of these methods, given that generalizable conclusions are fundamental to the utility of their results.