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Piecewise regression6/28/2023 ![]() By making a few simple changes to the data set-up and model specification, segmented regression analysis can easily be implemented in standard statistical software packages. Statistical tests of changes in intercepts and slopes pre- to post-intervention are carried out. In a basic segmented regression analysis, the time period is divided into pre- and post-intervention segments, and separate intercepts and slopes are estimated in each segment. Estimating the intervention effect is done by comparing the trend in the outcome after the intervention to the existing trend in the pre-intervention period, and is achieved through modifications to the standard regression analysis. A major strength of this design is its ability to distinguish the effect of the intervention from secular change, that is, change that would have happened even in the absence of the intervention. In an ITS study, a series of observations on the same outcome before and after the introduction of an intervention are used to test immediate and gradual effects of the intervention. Several modifications to the basic segmented regression analysis approach are available to deal with challenges arising in the evaluation of complex quality improvement interventions.Īn Interrupted Time Series (ITS) study is a powerful quasi-experimental design for evaluating effects of interventions when random assignment is not feasible. Segmented regression analysis is the recommended approach for analysing data from an interrupted time series study. Findingsīased 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. ![]() 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. We contrast the results from this standard regression analysis with those from segmented regression analysis. In the original analysis, a standard regression model was used with time as a continuous variable. 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 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. To utilize the strength of this design, a modification to standard regression analysis, such as segmented regression, is required. An interrupted time series design is a powerful quasi-experimental approach for evaluating effects of interventions introduced at a specific point in time.
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