Interrupted time series analysis based on hierarchical data_Chinese Journal of Evidence-Based Medicine

Authors：

ZHANG Yuanjin ^1,2,3,4 , REN Yan ^1,2,3,4 , YAO Minghong ^1,2,3,4 , HUANG Yunxiang ^1,2,3,4 , JIA Yulong ^1,2,3,4 , WANG Yuning ^1,2,3,4 , ZOU Kang ^1,2,3,4 ,  SUN Xin ^1,2,3,4

1. Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, P. R. China;
3. Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, P. R. China;
4. Hainan Healthcare Security Administration Key Laboratory for Real World Data Research, Chengdu 610041, P. R. China;

Corresponding author：

SUN Xin, Email: sunxin@wchscu.cn

Keywords：

Interrupted time series analysis; Hierarchical data; Two-stage model; Multilevel model

DOI：

10.7507/1672-2531.202206098

Video：

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Abstract Full text Figures/Tables Video References Cited by

Interrupted time series (ITS) analysis is a quasi-experimental design for evaluating the effectiveness of health interventions. By controlling the time trend before the intervention, ITS is often used to estimate the level change and slope change after the intervention. However, the traditional ITS modeling strategy might indicate aggregation bias when the data was collected from different clusters. This study introduced two advanced ITS methods of handling hierarchical data to provide the methodology framework for population-level health intervention evaluation.

Citation： ZHANG Yuanjin, REN Yan, YAO Minghong, HUANG Yunxiang, JIA Yulong, WANG Yuning, ZOU Kang, SUN Xin. Interrupted time series analysis based on hierarchical data. Chinese Journal of Evidence-Based Medicine, 2022, 22(12): 1459-1466. doi: 10.7507/1672-2531.202206098 Copy

1.	于石成, 王琦琦, 毛凡, 等. 中断时间序列设计及其分析方法. 中华预防医学杂志, 2019, (8): 858-864.
2.	张华, 陶立元, 赵一鸣. 中断时间序列的基本理论及应用. 中华儿科杂志, 2019, (7): 531.
3.	李洋, 于石成, 金承刚, 等. 两组中断时间序列设计及其分析方法. 中华流行病学杂志, 2019, 40(9): 1159-1163.
4.	邵华, 王琦琦, 胡跃华, 等. 中断时间序列分析及其在公共卫生领域中的应用. 中华流行病学杂志, 2015, 36(9): 1015-1017.
5.	Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol, 2017, 46(1): 348-355.
6.	朱星月, 林腾飞, 米源, 等. 间断时间序列模型及其在卫生政策干预效果评价中的应用. 中国药事, 2018, 32(11): 1531-1540.
7.	董珊珊, 黄文治, 张伟. 中断时间序列分析在医院科研激励中的应用. 中国循证医学杂志, 2021, 21(9): 1098-1101.
8.	Kontopantelis E, Doran T, Springate DA, et al. Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 2015, 350: h2750.
9.	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.
10.	Jandoc R, Burden AM, Mamdani M, et al. Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. J Clin Epidemiol, 2015, 68(8): 950-956.
11.	Ewusie JE, Thabane L, Beyene J, et al. Multicenter interrupted time series analysis: incorporating within and between-center heterogeneity. Clin Epidemiol, 2020, 12: 625-636.
12.	Ewusie J, Beyene J, Thabane L, et al. An improved method for analysis of interrupted time series (ITS) data: accounting for patient heterogeneity using weighted analysis. Int J Biostat, 2021, Epub ahead of print. doi: 10.1515/ijb-2020-0046.
13.	Nichols J, Mamdani M, Gomes T, et al. Impact of clinical practice guidelines on blood glucose test strip prescription rates in manitoba and saskatchewan (Canada): an interrupted time-series analysis. Can J Diabetes, 2021, 45(6): 557-565.
14.	Ancker JS, Gossey JT, Nosal S, et al. Effect of an electronic health record "nudge" on opioid prescribing and electronic health record keystrokes in ambulatory care. J Gen Intern Med, 2021, 36(2): 430-437.
15.	Ju C, Wei L, Mackenzie IS, et al. Changes in prescribing rates of sodium-containing medications in the UK from 2009 to 2018: a cross-sectional study with interrupted time series analysis. BMJ Open, 2021, 11(2): e043566.
16.	Peek N, Gude WT, Keers RN, et al. Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in UK primary care: an interrupted time series analysis. PLoS Med, 2020, 17(10): e1003286.
17.	Scortichini M, Schneider Dos Santos R, De' Donato F, et al. Excess mortality during the COVID-19 outbreak in Italy: a two-stage interrupted time-series analysis. Int J Epidemiol, 2021, 49(6): 1909-1917.
18.	Onozuka D, Tanoue Y, Nomura S, et al. Reduced mortality during the COVID-19 outbreak in Japan, 2020: a two-stage interrupted time-series design. Int J Epidemiol, 2022, 51(1): 75-84.
19.	Liyanage P, Rocklöv J, Tissera HA. The impact of COVID-19 lockdown on dengue transmission in Sri Lanka: a natural experiment for understanding the influence of human mobility. PLoS Negl Trop Dis, 2021, 15(6): e0009420.
20.	Xiao H, Dai X, Wagenaar BH, et al. The impact of the COVID-19 pandemic on health services utilization in China: Time-series analyses for 2016-2020. Lancet Reg Health West Pac, 2021, 9: 100122.
21.	Kaiser SV, Jennings B, Rodean J, et al. Pathways for improving inpatient pediatric asthma care (PIPA): a multicenter, national study. Pediatrics, 2020, 145(6): e20193026.
22.	Kramer D, Droomers M, Jongeneel-Grimen B, et al. The impact of area-based initiatives on physical activity trends in deprived areas; a quasi-experimental evaluation of the Dutch District Approach. Int J Behav Nutr Phys Act, 2014, 11(1): 36.
23.	Prats-Uribe A, Orcau A, Millet JP, et al. Impact of socio-economic inequities on tuberculosis in a Southern European city: what is the effect of the recession. Int J Tuberc Lung Dis, 2019, 23(1): 45-51.
24.	Doran T, Kontopantelis E, Valderas JM, et al. Effect of financial incentives on incentivised and non-incentivised clinical activities: longitudinal analysis of data from the UK quality and outcomes framework. BMJ, 2011, 342: d3590.
25.	Ewusie JE, Soobiah C, Blondal E, et al. Methods, applications and challenges in the analysis of interrupted time series data: a scoping review. J Multidiscip Healthc, 2020, 13: 411-423.

1. 于石成, 王琦琦, 毛凡, 等. 中断时间序列设计及其分析方法. 中华预防医学杂志, 2019, (8): 858-864.
2. 张华, 陶立元, 赵一鸣. 中断时间序列的基本理论及应用. 中华儿科杂志, 2019, (7): 531.
3. 李洋, 于石成, 金承刚, 等. 两组中断时间序列设计及其分析方法. 中华流行病学杂志, 2019, 40(9): 1159-1163.
4. 邵华, 王琦琦, 胡跃华, 等. 中断时间序列分析及其在公共卫生领域中的应用. 中华流行病学杂志, 2015, 36(9): 1015-1017.
5. Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol, 2017, 46(1): 348-355.
6. 朱星月, 林腾飞, 米源, 等. 间断时间序列模型及其在卫生政策干预效果评价中的应用. 中国药事, 2018, 32(11): 1531-1540.
7. 董珊珊, 黄文治, 张伟. 中断时间序列分析在医院科研激励中的应用. 中国循证医学杂志, 2021, 21(9): 1098-1101.
8. Kontopantelis E, Doran T, Springate DA, et al. Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 2015, 350: h2750.
9. 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.
10. Jandoc R, Burden AM, Mamdani M, et al. Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. J Clin Epidemiol, 2015, 68(8): 950-956.
11. Ewusie JE, Thabane L, Beyene J, et al. Multicenter interrupted time series analysis: incorporating within and between-center heterogeneity. Clin Epidemiol, 2020, 12: 625-636.
12. Ewusie J, Beyene J, Thabane L, et al. An improved method for analysis of interrupted time series (ITS) data: accounting for patient heterogeneity using weighted analysis. Int J Biostat, 2021, Epub ahead of print. doi: 10.1515/ijb-2020-0046.
13. Nichols J, Mamdani M, Gomes T, et al. Impact of clinical practice guidelines on blood glucose test strip prescription rates in manitoba and saskatchewan (Canada): an interrupted time-series analysis. Can J Diabetes, 2021, 45(6): 557-565.
14. Ancker JS, Gossey JT, Nosal S, et al. Effect of an electronic health record "nudge" on opioid prescribing and electronic health record keystrokes in ambulatory care. J Gen Intern Med, 2021, 36(2): 430-437.
15. Ju C, Wei L, Mackenzie IS, et al. Changes in prescribing rates of sodium-containing medications in the UK from 2009 to 2018: a cross-sectional study with interrupted time series analysis. BMJ Open, 2021, 11(2): e043566.
16. Peek N, Gude WT, Keers RN, et al. Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in UK primary care: an interrupted time series analysis. PLoS Med, 2020, 17(10): e1003286.
17. Scortichini M, Schneider Dos Santos R, De' Donato F, et al. Excess mortality during the COVID-19 outbreak in Italy: a two-stage interrupted time-series analysis. Int J Epidemiol, 2021, 49(6): 1909-1917.
18. Onozuka D, Tanoue Y, Nomura S, et al. Reduced mortality during the COVID-19 outbreak in Japan, 2020: a two-stage interrupted time-series design. Int J Epidemiol, 2022, 51(1): 75-84.
19. Liyanage P, Rocklöv J, Tissera HA. The impact of COVID-19 lockdown on dengue transmission in Sri Lanka: a natural experiment for understanding the influence of human mobility. PLoS Negl Trop Dis, 2021, 15(6): e0009420.
20. Xiao H, Dai X, Wagenaar BH, et al. The impact of the COVID-19 pandemic on health services utilization in China: Time-series analyses for 2016-2020. Lancet Reg Health West Pac, 2021, 9: 100122.
21. Kaiser SV, Jennings B, Rodean J, et al. Pathways for improving inpatient pediatric asthma care (PIPA): a multicenter, national study. Pediatrics, 2020, 145(6): e20193026.
22. Kramer D, Droomers M, Jongeneel-Grimen B, et al. The impact of area-based initiatives on physical activity trends in deprived areas; a quasi-experimental evaluation of the Dutch District Approach. Int J Behav Nutr Phys Act, 2014, 11(1): 36.
23. Prats-Uribe A, Orcau A, Millet JP, et al. Impact of socio-economic inequities on tuberculosis in a Southern European city: what is the effect of the recession. Int J Tuberc Lung Dis, 2019, 23(1): 45-51.
24. Doran T, Kontopantelis E, Valderas JM, et al. Effect of financial incentives on incentivised and non-incentivised clinical activities: longitudinal analysis of data from the UK quality and outcomes framework. BMJ, 2011, 342: d3590.
25. Ewusie JE, Soobiah C, Blondal E, et al. Methods, applications and challenges in the analysis of interrupted time series data: a scoping review. J Multidiscip Healthc, 2020, 13: 411-423.

Chinese Journal of Evidence-Based Medicine

Interrupted time series analysis based on hierarchical data

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