ArticleBMC medical research methodology2024
Design and statistical analysis reporting among interrupted time series studies in drug utilization research: a cross-sectional survey.
Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Actionable osteoporosis alerts in musculoskeletal radiology reports improve secondary fracture prevention after orthopaedic fragility fractures.Archives of osteoporosis · 2026Article
- The long-term impact of the COVID-19 pandemic on mental and psychiatric service utilization in Iran: a 7-year longitudinal study from 2017 to 2024.BMC psychiatry · 2026Article
- Telemedicine implementation and healthcare utilization in Rwanda: interrupted time series of babyl digital health services from 2015 to 2024.BMC primary care · 2026Article
- Effect of Sodium Zirconium Cyclosilicate Approval on the Management of Acute Hyperkalemia in Japan: Interrupted Time Series Analysis.American journal of nephrology · 2026Article
- Impact of a multicomponent intervention on maternal Tdap vaccination coverage during a major pertussis resurgence in the province of Quebec, Canada: A quasi-experimental study.Human vaccines & immunotherapeutics · 2025Article
- People and Pets in the COVID-19 Pandemic and the Cost-of-Living Crisis: Identifying Trends in the Intake, Adoption and Return of Companion Animals During Times of Uncertainty.Animals : an open access journal from MDPI · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
introductionInterrupted time series (ITS) design is a commonly used method for evaluating large-scale interventions in clinical practice or public health. However, improperly using this method can lead to biased results.
objectiveTo investigate design and statistical analysis characteristics of drug utilization studies using ITS design, and give recommendations for improvements.
methodsA literature search was conducted based on PubMed from January 2021 to December 2021. We included original articles that used ITS design to investigate drug utilization without restriction on study population or outcome types. A structured, pilot-tested questionnaire was developed to extract information regarding study characteristics and details about design and statistical analysis.
resultsWe included 153 eligible studies. Among those, 28.1% (43/153) clearly explained the rationale for using the ITS design and 13.7% (21/153) clarified the rationale of using the specified ITS model structure. One hundred and forty-nine studies used aggregated data to do ITS analysis, and 20.8% (31/149) clarified the rationale for the number of time points. The consideration of autocorrelation, non-stationary and seasonality was often lacking among those studies, and only 14 studies mentioned all of three methodological issues. Missing data was mentioned in 31 studies. Only 39.22% (60/153) reported the regression models, while 15 studies gave the incorrect interpretation of level change due to time parameterization. Time-varying participant characteristics were considered in 24 studies. In 97 studies containing hierarchical data, 23 studies clarified the heterogeneity among clusters and used statistical methods to address this issue.
conclusionThe quality of design and statistical analyses in ITS studies for drug utilization remains unsatisfactory. Three emerging methodological issues warranted particular attention, including incorrect interpretation of level change due to time parameterization, time-varying participant characteristics and hierarchical data analysis. We offered specific recommendations about the design, analysis and reporting of the ITS study.
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Registered trials
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