London School of Hygiene and Tropical Medicine (LSHTM), London WC1E 7HT, UK.
School of Tropical Medicine and Global Health (TMGH), Nagasaki University, Nagasaki 852-8521, Japan.
Int J Environ Res Public Health. 2021 Dec 20;18(24):13398. doi: 10.3390/ijerph182413398.
While state sequence analysis (SSA) has been long used in social sciences, its use in pharmacoepidemiology is still in its infancy. Indeed, this technique is relatively easy to use, and its intrinsic visual nature may help investigators to untangle the latent information within prescription data, facilitating the individuation of specific patterns and possible inappropriate use of medications. In this paper, we provide an educational primer of the most important learning concepts and methods of SSA, including measurement of dissimilarities between sequences, the application of clustering methods to identify sequence patterns, the use of complexity measures for sequence patterns, the graphical visualization of sequences, and the use of SSA in predictive models. As a worked example, we present an application of SSA to opioid prescription patterns in patients with non-cancer pain, using real-world data from Italy. We show how SSA allows the identification of patterns in prescriptions in these data that might not be evident using standard statistical approaches and how these patterns are associated with future discontinuation of opioid therapy.
虽然状态序列分析 (SSA) 在社会科学中已经使用了很长时间，但它在药物流行病学中的应用仍处于起步阶段。实际上，这种技术相对容易使用，其内在的可视化性质可以帮助研究人员梳理处方数据中的潜在信息，便于识别特定模式和可能的药物不当使用。在本文中，我们提供了 SSA 最重要的学习概念和方法的教育入门，包括测量序列之间的差异、应用聚类方法识别序列模式、使用复杂性度量来衡量序列模式、序列的图形可视化以及 SSA 在预测模型中的应用。作为一个实例，我们使用来自意大利的真实世界数据，展示了 SSA 在非癌痛患者阿片类药物处方模式中的应用。我们展示了 SSA 如何允许识别这些数据中可能使用标准统计方法无法发现的处方模式，以及这些模式如何与阿片类药物治疗的未来停药相关。
