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

Evangelos Kontopantelis; Tim Doran; David A Springate; Iain Buchan; David Reeves

doi:10.1136/bmj.h2750

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

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

Authors

Evangelos Kontopantelis¹, Tim Doran², David A Springate³, Iain Buchan⁴, David Reeves³

Affiliations

¹ Centre for Health Informatics, Institute of Population Health, University of Manchester, Manchester M13 9GB, UK NIHR School for Primary Care Research, Centre for Primary Care, Institute of Population Health, University of Manchester, UK [email protected].
² Department of Health Sciences, University of York, UK.
³ NIHR School for Primary Care Research, Centre for Primary Care, Institute of Population Health, University of Manchester, UK Centre for Biostatistics, Institute of Population Health, University of Manchester.
⁴ Centre for Health Informatics, Institute of Population Health, University of Manchester, Manchester M13 9GB, UK.

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

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

Authors

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Abstract

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