Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review
Bibliographic record
Abstract
Joycelyne E Ewusie,1 Charlene Soobiah,2,3 Erik Blondal,2,3 Joseph Beyene,1 Lehana Thabane,1,4 Jemila S Hamid1,5 1Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; 2Li Ka Shing Knowledge Institute of St Michael’s Hospital, Toronto, Ontario, Canada; 3Institute of Health Policy Management and Evaluation (IHPME), University of Toronto, Toronto, ON, Canada; 4Biostatistics Unit, Father Sean O’Sullivan Research Centre, St Joseph’s Healthcare, Hamilton, Ontario, Canada; 5Clinical Research Unit, Children’s Hospital of Eastern Ontario, Ottawa, ON, CanadaCorrespondence: Jemila S HamidChildren’s Hospital of Eastern Ontario, 401 Smyth Road, Ottawa, ON K1H 8L1, CanadaTel +1 (613) 737-7600 x 4194Email jhamid@uottawa.caObjective: 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.Keywords: interrupted time series, segmented linear regression, ARIMA, limitations, methods, scoping review
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.417 | 0.686 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.026 | 0.039 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".