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Network Analysis of Clinical Trials on Depression: Implications for Comparative Effectiveness Research

机译:抑郁症临床试验的网络分析:对比较有效性研究的启示

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摘要

A comprehensive understanding of evidence related to treatments for a disease is critical for planning effective clinical care, and for designing future trials. However, it is often difficult to comprehend the available evidence because of the complex combination of interventions across trials, in addition to the limited search and retrieval tools available in databases such as ClinicalTrials.gov. Here we demonstrate the use of networks to visualize and quantitatively analyze the co-occurrence of drug interventions across trials on depression in ClinicalTrials.gov. The analysis identified general co-occurrence patterns of interventions across all depression trials, and specific co-occurrence patterns related to antidepressants and natural supplements. These results led to insights about the current state of depression trials, and to a graph-theoretic measure to categorize interventions for a disease. We conclude by discussing the opportunities and challenges of generalizing our approach to analyze comparative interventional studies for any disease.
机译:全面了解与疾病治疗有关的证据,对于规划有效的临床护理以及设计未来的试验至关重要。但是,除了跨临床(如ClinicalTrials.gov)数据库中有限的搜索和检索工具之外,由于跨试验的干预措施的复杂组合,通常很难理解可用证据。在这里,我们演示了如何使用网络可视化并定量分析ClinicalTrials.gov中针对抑郁症的各个试验中药物干预的同时发生。该分析确定了所有抑郁试验中干预措施的一般同时发生模式,以及与抗抑郁药和天然补品有关的特定同时发生模式。这些结果使人们对抑郁症试验的当前状态有了见识,并提出了一种图论方法来对疾病的干预措施进行分类。最后,我们讨论了推广针对任何疾病的比较性干预研究进行分析的方法所面临的机遇和挑战。

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