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Implement Sci. 2014 Jun 19;9:77. doi: 10.1186/1748-5908-9-77.

The use of segmented regression in analysing interrupted time series studies: an example in pre-hospital ambulance care.

Author information

1
Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa Hospital, 1053 Carling Avenue Admin Services Building, ASB 2-004, Civic Box 693, Ottawa, ON K1Y 4E9, Canada. mtaljaard@ohri.ca.

Abstract

BACKGROUND:

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.

FINDINGS:

Based on the estimated change in intercept and slope from pre- to post-intervention using segmented regression, we found insufficient evidence of a statistically significant effect on quality of care for stroke, although potential clinically important effects for AMI cannot be ruled out.

CONCLUSIONS:

Segmented regression analysis is the recommended approach for analysing data from an interrupted time series study. Several modifications to the basic segmented regression analysis approach are available to deal with challenges arising in the evaluation of complex quality improvement interventions.

PMID:
24943919
PMCID:
PMC4068621
DOI:
10.1186/1748-5908-9-77
[Indexed for MEDLINE]
Free PMC Article

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