Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions.
Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions.
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DOI：
10.1186/s12874-021-01235-8
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发表时间：
2021-03-22
影响因子：
4
通讯作者：
Pearson SA
中科院分区：
文献类型：
--
作者：
Schaffer AL;Dobbins TA;Pearson SA
中断时间序列分析越来越多地用于评估大规模卫生干预措施的影响。虽然分段回归是一种常见的方法，但它并不总是足够的，尤其是在存在季节性和自相关性的情况下。自回归综合移动平均（ARIMA）模型是一种可以解决这些问题的替代方法。我们描述了ARIMA模型背后的基本理论，以及它们如何用于评估人口层面的干预措施，如卫生政策的引入。我们讨论了如何选择形状的影响，模型选择过程，传递函数，检查模型拟合，并解释结果。我们还提供R和SAS代码来复制我们的结果。我们举例说明ARIMA模型使用的政策干预，以减少不适当的处方。2014年1月，澳大利亚政府取消了抗精神病药物quetioline的25 mg片剂规格的处方补充剂，以阻止其用于未经批准的适应症。我们研究的影响，这一政策的干预，分配quetiblide使用配药索赔数据。ARIMA模型是在其他方法不适用时评估大规模干预措施影响的有用工具，因为它可以说明基本趋势、自相关性和季节性，并允许对不同类型的影响进行灵活建模。在线版本包含补充材料，可通过10.1186/s12874-021-01235-8获得。
Interrupted time series analysis is increasingly used to evaluate the impact of large-scale health interventions. While segmented regression is a common approach, it is not always adequate, especially in the presence of seasonality and autocorrelation. An Autoregressive Integrated Moving Average (ARIMA) model is an alternative method that can accommodate these issues. We describe the underlying theory behind ARIMA models and how they can be used to evaluate population-level interventions, such as the introduction of health policies. We discuss how to select the shape of the impact, the model selection process, transfer functions, checking model fit, and interpretation of findings. We also provide R and SAS code to replicate our results. We illustrate ARIMA modelling using the example of a policy intervention to reduce inappropriate prescribing. In January 2014, the Australian government eliminated prescription refills for the 25 mg tablet strength of quetiapine, an antipsychotic, to deter its prescribing for non-approved indications. We examine the impact of this policy intervention on dispensing of quetiapine using dispensing claims data. ARIMA modelling is a useful tool to evaluate the impact of large-scale interventions when other approaches are not suitable, as it can account for underlying trends, autocorrelation and seasonality and allows for flexible modelling of different types of impacts. The online version contains supplementary material available at 10.1186/s12874-021-01235-8.
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影响因子：
5.8
作者：
Liboschik, Tobias;Fokianos, Konstantinos;Fried, Roland
通讯作者：
Fried, Roland
影响因子：
4
作者：
Gilmour, Stuart;Degenhardt, Louisa;Day, Carolyn
通讯作者：
Day, Carolyn
影响因子：
4.5
作者：
Schaffer A;Muscatello D;Cretikos M;Gilmour R;Tobin S;Ward J
通讯作者：
Ward J
影响因子：
2.6
作者：
Bodkergaard, Katrine;Selmer, Randi M.;Stovring, Henrik
通讯作者：
Stovring, Henrik
影响因子：
5.8
作者：
Hyndman, Rob J.;Khandakar, Yeasmin
通讯作者：
Khandakar, Yeasmin