Methodological study reveals common discrepancies in design and analysis reporting of ITS studies, indicating the need for improved protocols.
Background Unexplained changes from a study’s analysis plan may increase the risk of bias in study results and undermine confidence in the findings. Discrepancies in methods between protocols and reports have not been examined in interrupted time series (ITS) studies. Methods We searched for peer-reviewed protocols of ITS studies in 22 databases, and results reports addressing each protocol’s primary research question. We compared 20 design and analysis items between protocols and their reports, classifying an item as ‘discrepant’ when the report provided different details about the item compared to the protocol. We judged a discrepancy as ‘potentially important’ if it could have potentially impacted the results or the study conclusion. We recorded if authors provided justifications for discrepancies. Results From 4,590 abstracts, after excluding ongoing studies, we identified 120 eligible ITS protocols, from which 44 protocols (37%) had at least one corresponding results report. Information about handling the complexities of time-series data were frequently missing from the protocol or report, or both, for example, methods of adjusting for autocorrelation (77% of studies, 34/44) and seasonality (82%, 36/44). Potentially important discrepancies were common for eligibility criteria (43%, 19/44), overall length of time series (39%, 17/44), length of each time segment (45%, 20/44), effect measures used to quantify the impact of the interruption (25%, 11/44) and ITS analysis methods used to estimate the model parameters (23%, 10/44). Among studies with important discrepancies, justifications were missing in 50%-100% of cases. Conclusion Study designs and analysis methods were often not, or insufficiently, reported in ITS studies. Changes to original analysis plans were also prevalent and often unjustified, precluding readers from judging the legitimacy of the changes. Protocols for ITS studies should provide detailed information about the design and analysis methods. Deviations from planned methods should be transparently reported with clear justifications.
No takes yet. Share an insight, caveat, or question.
A 2026 study studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: