Ewusie Joycelyne E, Blondal Erik, Soobiah Charlene, Beyene Joseph, Thabane Lehana, Straus Sharon E, Hamid Jemila S
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.
Li Ka Shing Knowledge Institute of St Michael's Hospital, Toronto, Canada.
BMJ Open. 2017 Jul 2;7(6):e016018. doi: 10.1136/bmjopen-2017-016018.
Interrupted time series (ITS) design involves collecting data across multiple time points before and after the implementation of an intervention to assess the effect of the intervention on an outcome. ITS designs have become increasingly common in recent times with frequent use in assessing impact of evidence implementation interventions. Several statistical methods are currently available for analysing data from ITS designs; however, there is a lack of guidance on which methods are optimal for different data types and on their implications in interpreting results. Our objective is to conduct a scoping review of existing methods for analysing ITS data, to summarise their characteristics and properties, as well as to examine how the results are reported. We also aim to identify gaps and methodological deficiencies.
We will search electronic databases from inception until August 2016 (eg, MEDLINE and JSTOR). Two reviewers will independently screen titles, abstracts and full-text articles and complete the data abstraction. The anticipated outcome will be a summarised description of all the methods that have been used in analysing ITS data in health research, how those methods were applied, their strengths and limitations and the transparency of interpretation/reporting of the results. We will provide summary tables of the characteristics of the included studies. We will also describe the similarities and differences of the various methods.
Ethical approval is not required for this study since we are just considering the methods used in the analysis and there will not be identifiable patient data. Results will be disseminated through open access peer-reviewed publications.
中断时间序列（ITS）设计涉及在实施一项干预措施之前和之后的多个时间点收集数据，以评估该干预措施对某一结果的影响。近年来，ITS设计越来越普遍，经常用于评估循证实施干预措施的影响。目前有几种统计方法可用于分析ITS设计的数据；然而，对于哪些方法最适用于不同的数据类型以及这些方法在解释结果时的意义，缺乏相关指导。我们的目的是对现有的ITS数据分析方法进行范围综述，总结其特点和属性，并研究结果是如何报告的。我们还旨在找出差距和方法上的不足之处。
我们将检索从数据库建立到2016年8月的电子数据库（如MEDLINE和JSTOR）。两名评审员将独立筛选标题、摘要和全文文章，并完成数据提取。预期结果将是对健康研究中用于分析ITS数据的所有方法的总结描述，这些方法是如何应用的，它们的优点和局限性，以及结果解释/报告的透明度。我们将提供纳入研究特征的汇总表。我们还将描述各种方法的异同。
由于我们仅考虑分析中使用的方法，且不会涉及可识别的患者数据，因此本研究无需伦理批准。结果将通过开放获取的同行评审出版物进行传播。
