INTERRUPTED TIME SERIES DESIGNS IN HEALTH TECHNOLOGY ASSESSMENT: LESSONS FROM TWO SYSTEMATIC REVIEWS OF BEHAVIOR CHANGE STRATEGIES
Bibliographic record
Abstract
OBJECTIVES: In an interrupted time series (ITS) design, data are collected at multiple instances over time before and after an intervention to detect whether the intervention has an effect significantly greater than the underlying secular trend. We critically reviewed the methodological quality of ITS designs using studies included in two systematic reviews (a review of mass media interventions and a review of guideline dissemination and implementation strategies). METHODS: Quality criteria were developed, and data were abstracted from each study. If the primary study analyzed the ITS design inappropriately, we reanalyzed the results by using time series regression. RESULTS: Twenty mass media studies and thirty-eight guideline studies were included. A total of 66% of ITS studies did not rule out the threat that another event could have occurred at the point of intervention. Thirty-three studies were reanalyzed, of which eight had significant preintervention trends. All of the studies were considered "effective" in the original report, but approximately half of the reanalyzed studies showed no statistically significant differences. CONCLUSIONS: We demonstrated that ITS designs are often analyzed inappropriately, underpowered, and poorly reported in implementation research. We have illustrated a framework for appraising ITS designs, and more widespread adoption of this framework would strengthen reviews that use ITS designs.
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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 | 0.675 | 0.868 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".