2015
DOI: 10.1136/bmj.h2750
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Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis

Abstract: Interrupted time series analysis is a quasi-experimental design that can evaluate an intervention effect, using longitudinal data. The advantages, disadvantages, and underlying assumptions of various modelling approaches are discussed using published examples

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Cited by 730 publications
(618 citation statements)
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“…(16) Our use of an interrupted time series approach that controls for baseline level and trend is considered a strong quasi-experimental modeling strategy, allowing for "real world" estimation of longitudinal effects when an RCT is unfeasible. (10,12) The reverse of this ecological correlation has also been previously demonstrated, ie, recent increases in hip fracture incidence alongside a reduction in bisphosphonate use, (17) itself likely due to reports of atypical femoral fractures associated with long-term bisphosphonate use. The large number of primary hip fractures and the generalizability of the CPRD cohort to the general UK population (9) are further strengths of the analysis.…”
Section: Discussionmentioning
confidence: 64%
“…(16) Our use of an interrupted time series approach that controls for baseline level and trend is considered a strong quasi-experimental modeling strategy, allowing for "real world" estimation of longitudinal effects when an RCT is unfeasible. (10,12) The reverse of this ecological correlation has also been previously demonstrated, ie, recent increases in hip fracture incidence alongside a reduction in bisphosphonate use, (17) itself likely due to reports of atypical femoral fractures associated with long-term bisphosphonate use. The large number of primary hip fractures and the generalizability of the CPRD cohort to the general UK population (9) are further strengths of the analysis.…”
Section: Discussionmentioning
confidence: 64%
“…The presence of seasonal and long-term trends complicates interrupted time series designs, but accounting for such factors in the pre-intervention period is the crucial assumption for the validity of any interrupted time series analysis [36,37,44,45]. We used an algorithm [41] to select the most parsimonious model based on pre-recession trends in each demographic group, and it seems plausible that our results differ from prior studies because the pre-intervention trends differ substantially by demographic group.…”
Section: Discussionmentioning
confidence: 99%
“…It is therefore able to provide more precise estimates than can be done using averaged or standardized temperatures. This can be done either with or without a control group that did not receive the intervention (Kontopantelis, Doran, Springate, Buchan, & Reeves, 2015). Known time-varying confounders (e.g.…”
Section: Present Studymentioning
confidence: 99%