Interrupted Time-Series Analysis

Last Updated: 01/08/2021

Beard. 2019. Understanding and using time series analyses in addiction research. Addiction, 114, pp.1866-1884.

 

Biglan et al. 2000. The Value of Interrupted Time-Series Experiments for Community Intervention Research. Prevention Science, 1(1), pp.31–49.

 

Bloom. 2003. Using "short" interrupted time-series analysis to measure the impacts of whole-school reforms. With applications to a study of accelerated schools. Evaluation Reviews, 1(3), p3-49.

Crosbie J. 1993. Interrupted time-series analysis with brief single-subject data. Journal of

Consulting and Clinical Psychology, 61, pp.966-974.

 

Kontopantelis et al. 2015. Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 350, h2750.

 

Linden. 2017. Challenges to validity in single-group interrupted time series analysis. Journal of Evaluation in Clinical Practice, 23(2), pp. 413-418.

 

Linden and Arbor. 2015. Conducting interrupted time-series analysis for single- and multiple-group comparisons. The Stata Journal, 15, 480–500.

 

Linden and Yarnold. 2016. Using machine learning to identify structural breaks in single-group interrupted time series designs. Journal of Evaluation in Clinical Practice, 22(6), pp.851-855.

 

Lopez Bernal et al. 2016. Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), pp.348-355.

 

Lopez Bernal et al. 2018. A methodological framework for model selection in interrupted time series studies. Journal of Clinical Epidemiology, 103, pp.82-91.

 

McDowall et al. 1980. Interrupted Time Series Analysis. Newbury Park, CA: Sage Publications, Inc.

 

Penfold and Zhang. 2013. Use of Interrupted Time Series Analysis in Evaluating Health Care Quality Improvements. Academic Pediatrics, 13(6), pp.38-44.

 

Schaffer et al. 2021. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Medical Research Methodology, 21, 58.

 

Wagner et al. 2002. Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27, pp.299-309.

 

ITSA with a control group:

 

Bottomley et al. 2019. Analysing Interrupted Time Series with a Control. Epidemiologic Methods, 8(1), pp.1-10.

Esposti et al. 2020. Can synthetic controls improve causal inference in interrupted time series evaluations of public health interventions. International Journal of Epidemiology, 49(6), pp.2010-2020.

 

Linden and Adams. 2011. Applying a propensity-score based weighting model to interrupted time series data: improving causal inference in program evaluation. Journal of Evaluation in Clinical Practice, 17, 1231–1238.

 

Lopez Bernal et al. 2018. The use of controls in interrupted time series studies of public health interventions. International Journal of Epidemiology, 47(6), pp.2082-2093.

 

Reviews of application

 

Ewusie et al. 2020. Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review. Journal of Multidisciplinary Healthcare, 13, pp. 411–423.

 

Hategeka et al. 2020. Use of interrupted time series methods in the evaluation of health system quality improvement interventions: a methodological systematic review. BMJ Global Heath, 5, e003567.

 

Hudson et al. 2019. Methodology and reporting characteristics of studies using interrupted time series design in healthcare. BMC Medical Research Methodology, 19, 137.

 

Jandoc et al. 2015. Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. Journal of Clinical Epidemiology, 68, pp.950-956.

 

Polus et al. 2017. Heterogeneity in application, design, and analysis characteristics was found for controlled before-after and interrupted time series studies included in Cochrane reviews. Journal of Clinical Epidemiology, 91, pp.56-69.

 

Turner et al. 2020. Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review. Journal of Clinical Epidemiology, 122, pp.1-11.

 

Turner et al. 2021. Creating effective interrupted time series graphs: Review and recommendations. Research Synthesis Methods, 12, pp.106-117.

 

Simulations and empirical testing

 

Baicker and Svoronos. 2019. Testing the validity of the single interrupted time series design. NBER Working Paper No. 26080.

 

Fretheim et al. 2013. Interrupted time-series analysis yielded an effect estimate concordant with the cluster-randomized controlled trial result. Journal of Clinical Epidemiology, 66(8), pp.883-887.

 

Fretheim et al. 2015. A reanalysis of cluster randomized trials showed interrupted time-series studies were valuable in health system evaluation. Journal of Clinical Epidemiology, 68(3), pp.324-333.

 

Hallberg et al. 2020. Improving the Use of Aggregate Longitudinal Data on School Performance to Assess Program Effectiveness: Evidence from Three Within Study Comparisons. Journal of Research on Educational Effectiveness, 13(3), pp.518-545.

 

Miratrix. 2020. Using Simulation to Analyze Interrupted Time Series Designs. Working Paper.

 

Shadish et al. 2016. Single-case experimental design yielded an effect estimate corresponding to a randomized controlled trial. Journal of Clinical Epidemiology, 76, pp.82-88.

 

St. Clair et al. 2014 Examining the Internal Validity and Statistical Precision of the Comparative Interrupted Time Series Design by Comparison With a Randomized Experiment. American Journal of Evaluation, 35(3), pp.311-327.

 

St. Clair et al. 2016. The Validity and Precision of the Comparative Interrupted Time-Series Design: Three Within-Study Comparisons. Journal of Educational and Behavioral Statistics, 41(3), pp.269–299.

 

Turner et al. 2021. Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series. BMC Medical Research Methodology, 21, 134.

 

Power calculation:

 

Bloom. 1999. Estimating program impacts on student achievement using “short” interrupted time series. Working Paper: Manpower Demonstration Research Corporation.

 

Hawley et al. 2019. Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study. Clinical Epidemiology, 11, pp.197–205.

 

Liu et al. 2020. Simulation-based power and sample size calculation for designing interrupted time series analyses of count outcomes in evaluation of health policy interventions. Contemporary clinical trials communications, 17, 100474.

 

Schochet. 2021. Statistical Power for Estimating Treatment Effects Using Difference-in-Differences and Comparative Interrupted Time Series Designs with Variation in Treatment Timing. Working Paper.

 

Zhang et al. 2009. Methods for estimating confidence intervals in interrupted time series analyses of health interventions. Journal of Clinical Epidemiology, 62(2), pp.143–148.

 

Zhang et al. 2011. Simulation-based power calculation for designing interrupted time series analyses of health policy interventions. Journal of Clinical Epidemiology, 64(11), pp.1252-1261.

 

Other applied topics:

 

Bazo-Alvarez et al. 2020. Handling Missing Values in Interrupted Time Series Analysis of Longitudinal Individual-Level Data. Clinical Epidemiology, 12, pp.1045–1057.

 

Zhang et al. 2009. Methods for estimating confidence intervals in interrupted time series analyses of health interventions. Journal of Clinical Epidemiology, 62(2), pp.143–148.