Division of Psychiatry, University College London, London, UK.
Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Basel, Switzerland.
Schizophr Bull. 2017 Oct 21;43(6):1273-1279. doi: 10.1093/schbul/sbx013.
Modern psychiatric epidemiology researches complex interactions between multiple variables in large datasets. This creates difficulties for causal inference. We argue for the use of probabilistic models represented by directed acyclic graphs (DAGs). These capture the dependence structure of multiple variables and, used appropriately, allow more robust conclusions about the direction of causation. We analyzed British national survey data to assess putative mediators of the association between bullying victimization and persecutory ideation. We compared results using DAGs and the Karlson-Holm-Breen (KHB) logistic regression commands in STATA. We analyzed data from the 2007 English National Survey of Psychiatric Morbidity, using the equivalent 2000 survey in an instant replication. Additional details of methods and results are provided in the supplementary material. DAG analysis revealed a richer structure of relationships than could be inferred using the KHB logistic regression commands. Thus, bullying had direct effects on worry, persecutory ideation, mood instability, and drug use. Depression, sleep and anxiety lay downstream, and therefore did not mediate the link between bullying and persecutory ideation. Mediation by worry and mood instability could not be definitively ascertained. Bullying led to hallucinations indirectly, via persecutory ideation and depression. DAG analysis of the 2000 dataset suggested the technique generates stable results. While causality cannot be fully determined from cross-sectional data, DAGs indicate the relationships providing the best fit. They thereby advance investigation of the complex interactions seen in psychiatry, including the mechanisms underpinning psychiatric symptoms. It may consequently be used to optimize the choice of intervention targets.
现代精神科流行病学研究在大型数据集内多种变量之间的复杂相互作用。这为因果推断带来了困难。我们主张使用有向无环图（DAG）表示的概率模型。这些模型捕捉了多个变量的依赖结构，并且如果使用得当，就可以对因果关系的方向得出更稳健的结论。我们分析了英国全国性调查数据，以评估欺凌受害者与迫害观念之间关联的潜在中介因素。我们使用 DAG 和 STATA 中的 Karlson-Holm-Breen（KHB）逻辑回归命令比较了结果。我们分析了 2007 年英国国家精神病理学调查的数据，并在即时复制中使用了等效的 2000 年调查。方法和结果的更多详细信息在补充材料中提供。DAG 分析揭示了比 KHB 逻辑回归命令推断更丰富的关系结构。因此，欺凌对担忧、迫害观念、情绪不稳定和药物使用有直接影响。抑郁、睡眠和焦虑处于下游，因此不会介导欺凌与迫害观念之间的联系。无法明确确定担忧和情绪不稳定的中介作用。欺凌通过迫害观念和抑郁间接导致幻觉。对 2000 年数据集的 DAG 分析表明，该技术产生了稳定的结果。虽然从横断面数据中不能完全确定因果关系，但 DAG 指示了提供最佳拟合的关系。它们从而推进了对精神病学中复杂相互作用的研究，包括精神病症状的潜在机制。因此，它可以用于优化干预目标的选择。
