Interrupted time series regression for the evaluation of public health interventions: a tutorial.
Interrupted time series regression for the evaluation of public health interventions: a tutorial.
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DOI：
10.1093/ije/dyw098
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发表时间：
2017-02-01
影响因子：
7.7
通讯作者：
Gasparrini A
中科院分区：
文献类型：
--
作者：
Bernal JL;Cummins S;Gasparrini A
中断时间序列（ITS）分析是一种有价值的研究设计，用于评估在明确定义的时间点实施的人群水平健康干预措施的有效性。它越来越多地用于评估从临床治疗到国家公共卫生立法等干预措施的有效性。虽然在其他流行病学研究中，该设计与基于回归的方法具有许多相同的特性，但时间序列数据有一系列独特的特征，需要额外的方法学考虑。在本教程中，我们将使用一个工作示例来演示使用分段回归进行ITS分析的鲁棒方法。我们开始通过描述设计和考虑当ITS是一个适当的设计选择。然后，我们讨论的基本，但往往被忽略，提出的影响模型的先验步骤。随后，我们展示了统计分析的方法，包括主要的分段回归模型。最后，我们描述了与ITS分析相关的主要方法问题：时间序列数据的过度分散，自相关，季节性趋势调整和随时间变化的混杂因素控制，我们还概述了一些更复杂的设计适应，可用于加强基本的ITS设计。
Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.