<p>Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review</p>

Abstract

Objective: 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. Design: Scoping review. Data Sources: Searches were conducted in MEDLINE, JSTOR, PUBMED, EMBASE, CINAHL, Web of Science and the Cochrane Library from inception until September 2017. Study Selection: Studies in health research involving ITS methods or reporting on the application of ITS designs. Data Extraction: 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. Results: 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. Conclusion: 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.

Use of interrupted time series methods in the evaluation of health system quality improvement interventions: a methodological systematic review

“…15 There remains a risk, however, of producing misleading or incorrect results when this rather robust design is not used appropriately. 16–18 …”

“…While there are many statistical approaches used for estimating effects of interventions in ITS studies, segmented regression analysis of ITS is the most commonly used, followed by autoregressive integrated moving average (ARIMA) models. 16 20 21 Details on segmented regression analysis can be found in the seminal paper of Wagner and colleagues. 18 While most ITS studies use segmented regression analysis on time series data aggregated within periods of time, analysis on individual-level data is feasible using mixed effects models.…”

Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study

“…Interrupted time series (ITS) studies are frequently used to evaluate the impact of interventions or exposures that occur at a particular point in time [1][2][3][4] . Although randomised trials are the gold standard study design, randomisation may be infeasible or undesirable in the case of policy evaluation or interventions that are implemented at a population level.…”

<p>MultiCenter Interrupted Time Series Analysis: Incorporating Within and Between-Center Heterogeneity</p>

“…[1][2][3][4] A recent scoping review, aimed at identifying methods for analyzing ITS data, showed that SR models were the most utilized methods, having been used in approximately 45% of the included studies to analyze their ITS data. 5 These methods have been applied in various health research areas (eg clinical research, public health, and health services) to assess the impact of interventions on patient important outcomes or clinical practice. [6][7][8][9][10][11][12] SR is a special case of multiple linear regression with an indicator variable representing the intervention periods, a continuous variable representing the time at which observations are taken, and an interaction variable.…”

“…Additionally, administrative routine data collected from different subjects across different regions are increasingly being used in ITS studies to perform post-hoc evaluation of nationwide policies and programs. 5 Such data are usually only available in aggregated forms.…”