ReviewDeutsches Arzteblatt international2024
Interrupted Time Series for Assessing the Causality of Intervention Effects. Part 35 of a Series on Evaluating Scientific Publications.
Review in Deutsches Arzteblatt international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Increase in Cannabis-Specific Hospital Admissions After Partial Legalization in Germany: A Nationwide Time-Series Analysis.Deutsches Arzteblatt international · 2026Observational
- Real-World Impact on Postoperative Vomiting by Changing Anesthesia Regimens in Children Undergoing Strabismus Surgery: An Interrupted Time Series Analysis.Paediatric anaesthesia · 2026Observational
- Take-Home Naloxone in Opioid Dependency: An Intervention to Reduce Opioid-Related Deaths.Deutsches Arzteblatt international · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe gold standard for evaluating interventions in medicine and health care is the randomized controlled trial (RCT). In practice, however, RCTs may be difficult to conduct because of high costs, ethical aspects, or practical considerations. This is particularly true of studies on the population level, e.g., for the evaluation of health policy measures.
methodsWe present a type of study design called the interrupted time series (ITS) and its critical interpretation, with several illustrative examples. This discussion is based on selected meth - odological publications.
resultsITS are suitable for the assessment of interventions with a clear point of intervention (interruption). They are analyzed with the statistical methods of time-series analysis. One strength of ITS is that they can be used to estimate an immediate effect as well as a gradually developing effect. Under certain assumptions, the findings of an ITS analysis can be interpreted causally. The main assumption underlying an ITS is that the trend after the intervention would have been exactly the same as the trend before the intervention if the intervention had not taken place and all other conditions had remained unchanged. A further assumption is that there should be no differences in the pre- versus postintervention phases in the subjects or other entities being tested (e.g., hospitals) that might affect the measured endpoints (e.g., differences in mean age affecting measured mortality). Moreover, the intervention periods must be properly distinct from one another in order to prevent biased effect estimates. The robustness of the assumptions should also be checked with sensitivity analyses.
conclusionAs long as all sources of bias have been avoided and the findings are both plausible and robust, the effects revealed by ITS can be interpreted as causal. ITS may serve as an alternative method for evaluating intervention effects when an RCT cannot be performed.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.