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. 2015 Jun 9:350:h2750.
doi: 10.1136/bmj.h2750.

Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis

Evangelos Kontopantelis  1 , Tim Doran  2 , David A Springate  3 , Iain Buchan  4 , David Reeves  3
Affiliations

Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis

Evangelos Kontopantelis et al. BMJ. .

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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Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: No relationships or activities not discussed in the funding statement that could appear to have influenced the submitted work.

Figures

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Fig 1 Interrupted time series analysis components in relation to the Quality and Outcomes Framework intervention
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Fig 2 Quality and Outcomes Framework (QOF) performance graphs for four presented examples. (A) Care for asthma, diabetes, and heart disease. Aggregate practice level performance across three clinical domains of interest. (B) Diabetes care by number of comorbidities. Aggregate patient level performance for patients in the diabetes domain, by number of additional conditions. (C) Incentivised and non-incentivised aspects of care. Aggregate practice level performance by incentivisation category and indicator type. (D) Blood pressure measurement indicators. Aggregate practice level performance on blood pressure measurement indicator. FI=fully incentivised, PI=partially incentivised, UI=unincentivised, PM/R=process measurement recording, PT=process treatment, I=intermediate outcome. The number of indicators in each group are in parentheses. CHD, DM, Stroke, and BP relate to the coronary heart disease, diabetes mellitus, stroke, and hypertension QOF clinical domains, respectively
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References

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