Ewusie Joycelyne E, Soobiah Charlene, Blondal Erik, Beyene Joseph, Thabane Lehana, Hamid Jemila S
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Li Ka Shing Knowledge Institute of St Michael's Hospital, Toronto, Ontario, Canada.
J Multidiscip Healthc. 2020 May 13;13:411-423. doi: 10.2147/JMDH.S241085. eCollection 2020.
Interrupted time series (ITS) designs are robust quasi-experimental designs commonly used to evaluate the impact of interventions and programs implemented in healthcare settings. This scoping review aims to 1) identify and summarize existing methods used in the analysis of ITS studies conducted in health research, 2) elucidate their strengths and limitations, 3) describe their applications in health research and 4) identify any methodological gaps and challenges.
Scoping review.
Searches were conducted in MEDLINE, JSTOR, PUBMED, EMBASE, CINAHL, Web of Science and the Cochrane Library from inception until September 2017.
Studies in health research involving ITS methods or reporting on the application of ITS designs.
Screening of studies was completed independently and in duplicate by two reviewers. One reviewer extracted the data from relevant studies in consultations with a second reviewer. Results of the review were presented with respect to methodological and application areas, and data were summarized using descriptive statistics.
A total of 1389 articles were included, of which 98.27% (N=1365) were application papers. Segmented linear regression was the most commonly used method (26%, N=360). A small percentage (1.73%, N=24) were methods papers, of which 11 described either the development of novel methods or improvement of existing methods, 7 adapted methods from other areas of statistics, while 6 provided comparative assessment of conventional ITS methods.
A significantly increasing trend in ITS use over time is observed, where its application in health research almost tripled within the last decade. Several statistical methods are available for analyzing ITS data. Researchers should consider the types of data and validate the required assumptions for the various methods. There is a significant methodological gap in ITS analysis involving aggregated data, where analyses involving such data did not account for heterogeneity across patients and hospital settings.
中断时间序列（ITS）设计是稳健的准实验设计，常用于评估在医疗环境中实施的干预措施和项目的影响。本范围综述旨在：1）识别并总结健康研究中进行的ITS研究分析中使用的现有方法；2）阐明其优势和局限性；3）描述其在健康研究中的应用；4）识别任何方法学差距和挑战。
范围综述。
从数据库创建至2017年9月，在MEDLINE、JSTOR、PUBMED、EMBASE、CINAHL、科学引文索引和考科蓝图书馆进行检索。
健康研究中涉及ITS方法或报告ITS设计应用的研究。
由两名评审员独立且重复地完成研究筛选。一名评审员在与另一名评审员协商后，从相关研究中提取数据。综述结果根据方法学和应用领域呈现，数据使用描述性统计进行总结。
共纳入1389篇文章，其中98.27%（N = 1365）为应用论文。分段线性回归是最常用的方法（26%，N = 360）。一小部分（1.73%，N = 24）是方法学论文，其中11篇描述了新方法的开发或现有方法的改进，7篇从其他统计学领域改编方法，而6篇对传统ITS方法进行了比较评估。
随着时间推移，ITS的使用呈现出显著增加的趋势，在过去十年中其在健康研究中的应用几乎增长了两倍。有几种统计方法可用于分析ITS数据。研究人员应考虑数据类型并验证各种方法所需的假设。在涉及汇总数据的ITS分析中存在显著的方法学差距，涉及此类数据的分析未考虑患者和医院环境之间的异质性。
