References
- Lopez Bernal J, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol. 2017;46:348–355. doi:10.1093/ije/dyw09827283160
- Jandoc R, Burden AM, Mamdani M, Lévesque LE, Cadarette SM. Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. J Clin Epidemiol. 2015;68:950–956. doi:10.1016/J.JCLINEPI.2014.12.01825890805
- Hudson J, Fielding S, Ramsay CR. Methodology and reporting characteristics of studies using interrupted time series design in healthcare. BMC Med Res Methodol. 2019;19:137. doi:10.1186/s12874-019-0777-x31272382
- Bao L, Peng R, Wang Y, et al. Significant Reduction of Antibiotic Consumption and Patients’ Costs after an Action Plan in China, 2010–2014. PLoS One. 2015;10:e0118868. doi:10.1371/journal.pone.011886825767891
- Yoo KB, Lee SG, Park S, et al. Effects of drug price reduction and prescribing restrictions on expenditures and utilisation of antihypertensive drugs in Korea. BMJ Open. 2015:5. doi:10.1136/bmjopen-2014-006940
- Uijtendaal EV, Zwart-van Rijkom JEF, de Lange DW, Lalmohamed A, van Solinge WW, Egberts TCG. Influence of a strict glucose protocol on serum potassium and glucose concentrations and their association with mortality in intensive care patients. Crit Care. 2015;19:270. doi:10.1186/s13054-015-0959-926100120
- Petersen I, Welch CA, Nazareth I, et al. Health indicator recording in UK primary care electronic health records: key implications for handling missing data. Clin Epidemiol. 2019;11:157–167. doi:10.2147/CLEP.S19143730809103
- Schaffer AL, Dobbins TA, Pearson SA. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med Res Methodol. 2021;21:1–12. doi:10.1186/s12874-021-01235-833397292
- Linden A. Conducting interrupted time-series analysis for single- and multiple-group comparisons. Stata J. 2015;15:480–500. doi:10.1177/1536867x1501500208
- Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;299–309. doi:10.1046/j.1365-2710.2002.00430.x12174032
- Polus S, Pieper D, Burns J, et al. Heterogeneity in application, design, and analysis characteristics was found for controlled before-after and interrupted time series studies included in Cochrane reviews. J Clin Epidemiol. 2017;91:56–69. doi:10.1016/J.JCLINEPI.2017.07.00828750849
- Turner SL, Karahalios A, Forbes AB, et al. Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review. J Clin Epidemiol. 2020;122:1–11. doi:10.1016/j.jclinepi.2020.02.00632109503
- Ewusie JE, Soobiah C, Blondal E, Beyene J, Thabane L, Hamid JS. Methods, applications and challenges in the analysis of interrupted time series data: a scoping review. J Multidiscip Healthc. 2020;13:411–423. doi:10.2147/JMDH.S24108532494150
- Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169:467–473. doi:10.7326/M18-085030178033
- Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18:143. doi:10.1186/s12874-018-0611-x30453902
- StataCorp. Stata Statistical Software. Release. 2019;16.
- Lopez Bernal J, Cummins S, Gasparrini A. The use of controls in interrupted time series studies of public health interventions. Int J Epidemiol. 2018;47:2082–2093. doi:10.1093/ije/dyy13529982445
- Close J, Fosh B, Wheat H, et al. Longitudinal evaluation of a countywide alternative to the Quality and Outcomes Framework in UK General Practice aimed at improving Person Centred Coordinated Care. BMJ Open. 2019;9:29721. doi:10.1136/bmjopen-2019-029721
- Lopez Bernal J, Soumerai S, Gasparrini A. A methodological framework for model selection in interrupted time series studies. J Clin Epidemiol. 2018;103:82–91. doi:10.1016/J.JCLINEPI.2018.05.02629885427
- Bazo-Alvarez JC, Morris TP, Pham TM, Carpenter JR, Petersen I. Handling Missing Values in Interrupted Time Series Analysis of Longitudinal Individual-Level Data. Clin Epidemiol. 2020;12:1045–1057. doi:10.2147/CLEP.S26642833116899
- Cook TD, Campbell DT, Shadish W. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin; 2002.
- Van Buuren S. Flexible Imputation of Missing Data. Boca Raton: Chapman and Hall/CRC; 2012.
- White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30:377–399. doi:10.1002/sim.406721225900
- Quartagno M, Grund S, Carpenter J. Jomo: a Flexible Package for Two-level Joint Modelling Multiple Imputation. J R Stat Soc. 2019;9.
- Rabe-Hesketh S, Skrondal A, Skrondal A. Multilevel and Longitudinal Modeling Using Stata. Texas: Stata Press Publication; 2008.
- Miller RL, Chiaramonte D, Strzyzykowski T, Sharma D, Anderson-Carpenter K, Fortenberry JD. Improving Timely Linkage to Care among Newly Diagnosed HIV-Infected Youth: results of SMILE. J Urban Heal. 2019;96:845–855. doi:10.1007/s11524-019-00391-z
- Cro S, Carpenter JR, Kenward MG. Information-anchored sensitivity analysis: theory and application. J R Stat Soc. 2019;182:623–645. doi:10.1111/rssa.12423
- Liberati A, Altman D, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Journal of Clinical Epidemiology. 2009;62(10)e1–e34.19631507