Segmented regression analysis of interrupted time series studies in medication use research

By A. Wagner, S. Soumerai, Fang Zhang, D. Ross-Degnan. Journal of Clinical Pharmacy and Therapeutics, 2002

3,120 citations recorded by the source corpus.

Interrupted time series design is the strongest, quasi‐experimental approach for evaluating longitudinal effects of interventions. Segmented regression analysis is a powerful statistical method for estimating intervention effects in interrupted time series studies. In this paper, we show how segmented regression analysis can be used to evaluate policy and educational interventions intended to improve the quality of medication use and/or contain costs.

Subfields: Economics and Econometrics, General Health Professions

View Paper: https://www.semanticscholar.org/paper/515adb58dc53f864edff93b3cfb909cbb82218c1

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Citations in the corpus

Identifying Platform Effects in Social Media Data (2016, methodology): “We found two alternative methodologies created speciﬁcally around time series, ‘interrupted time series analysis’ (McDowall et al. 1980; Wagner et al. 2002; Taljaard et al. 2014) and ‘event studies’ (MacKinlay 1997), but both are essentially less formal versions of RD design and still neither…”
The use of controls in interrupted time series studies of public health interventions. (2018, methodology): “(6-9) Nevertheless, while the basic ITS design has been described in detail elsewhere and reference is made to the inclusion of a control as a method of improving the validity of the design,(2, 10) there is little guidance available on the what a control series can and cannot solve and how to select”
Interrupted time series regression for the evaluation of public health interventions: a tutorial (2016, methodology): “The interrupted time series (ITS) study design is increasingly being used for the evaluation of public health interventions; it is particularly suited to interventions introduced at a population level over a clearly defined time period and that target population-level health outcomes.(1,5) ITS has b”
Use of interrupted time series analysis in evaluating health care quality improvements. (2013, background): “ITS does not require adjustment for individual-level characteristics.(4) There are 3 important threats to validity in ITS analyses.”
An introduction to implementation science for the non-specialist (2015, background): “Such designs are most feasible when the implementation strategy is a discrete event, such as a policy change [57] since Fig.”
The use and interpretation of quasi-experimental studies in medical informatics. (2006, background): “In addition, statistically, there is a more robust analytic capability, and there is the ability to detect changes in the slope or intercept as a result of the intervention in addition to a change in the mean values.(18) A change in intercept could represent an immediate effect while a change in slo”
Implementing an Antibiotic Stewardship Program: Guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America. (2016, background): “Analyses that measure the effects of an intervention over time should compare actual costs after the initiation of the intervention vs projected costs in the absence of the intervention, as direct cost reductions tend to plateau [185, 186].”
Experimental and Quasi-Experimental Designs in Implementation Research (2019, background): “More formally, ITS evaluations focus on identifying whether there is discontinuity in the trend (change in slope or level) after the intervention relative to before the intervention, using segmented regression to model pre- and post-intervention trends (Gebski et al., 2012; Penfold and Zhang, 2013; ”