Title |
The use of segmented regression in analysing interrupted time series studies: an example in pre-hospital ambulance care
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Published in |
Implementation Science, June 2014
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DOI | 10.1186/1748-5908-9-77 |
Pubmed ID | |
Authors |
Monica Taljaard, Joanne E McKenzie, Craig R Ramsay, Jeremy M Grimshaw |
Abstract |
An interrupted time series design is a powerful quasi-experimental approach for evaluating effects of interventions introduced at a specific point in time. To utilize the strength of this design, a modification to standard regression analysis, such as segmented regression, is required. In segmented regression analysis, the change in intercept and/or slope from pre- to post-intervention is estimated and used to test causal hypotheses about the intervention. We illustrate segmented regression using data from a previously published study that evaluated the effectiveness of a collaborative intervention to improve quality in pre-hospital ambulance care for acute myocardial infarction (AMI) and stroke. In the original analysis, a standard regression model was used with time as a continuous variable. We contrast the results from this standard regression analysis with those from segmented regression analysis. We discuss the limitations of the former and advantages of the latter, as well as the challenges of using segmented regression in analysing complex quality improvement interventions. |
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