Gilmour Stuart, Degenhardt Louisa, Hall Wayne, Day Carolyn
National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW 2052, Australia.
BMC Med Res Methodol. 2006 Apr 3;6:16. doi: 10.1186/1471-2288-6-16.
Intervention time series analysis (ITSA) is an important method for analysing the effect of sudden events on time series data. ITSA methods are quasi-experimental in nature and the validity of modelling with these methods depends upon assumptions about the timing of the intervention and the response of the process to it.
This paper describes how to apply ITSA to analyse the impact of unplanned events on time series when the timing of the event is not accurately known, and so the problems of ITSA methods are magnified by uncertainty in the point of onset of the unplanned intervention.
The methods are illustrated using the example of the Australian Heroin Shortage of 2001, which provided an opportunity to study the health and social consequences of an abrupt change in heroin availability in an environment of widespread harm reduction measures.
Application of these methods enables valuable insights about the consequences of unplanned and poorly identified interventions while minimising the risk of spurious results.
干预时间序列分析（ITSA）是分析突发事件对时间序列数据影响的重要方法。ITSA方法本质上是准实验性的，使用这些方法进行建模的有效性取决于关于干预时间和过程对其反应的假设。
本文描述了在事件发生时间不准确已知的情况下，如何应用ITSA来分析意外事件对时间序列的影响，因此意外干预起始点的不确定性会放大ITSA方法的问题。
以2001年澳大利亚海洛因短缺为例对这些方法进行了说明，该事件提供了一个机会，来研究在广泛实施减少伤害措施的环境中，海洛因供应突然变化所带来的健康和社会后果。
应用这些方法能够深入了解意外且识别不清的干预措施所带来的后果，同时将虚假结果的风险降至最低。
