Common statistical methods used in quasi-experimental studies and applications in medicine_Chinese Journal of Evidence-Based Medicine

Authors：

LUO Xiao , HE Qian , QIN Yingyi ,  WU Cheng ,  HE Jia

Department of Military Health Statistics, Naval Medical University, Shanghai 200433, P. R. China;

Corresponding author：

WU Cheng, Email: wucheng_wu@126.com; HE Jia, Email: hejia63@yeah.net

Keywords：

Quasi-experimental study; Confounding factor; Adjustment; Statistical method

DOI：

10.7507/1672-2531.202204020

Video：

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Randomized controlled trials (RCTs) are often limited because of ethical or operational reasons. Quasi-experimental studies could be an alternative to RCTs to make causal inferences without randomization by controlling the confounding effects of the study. This paper introduced the general statistical analysis methods of quasi-experimental design through basic ideas, characteristics, limitations and applications in medicine, including difference-in-difference models, instrumental variables, regression discontinuity design, interrupted time series, and so on, and to provide references for future research.

Citation： LUO Xiao, HE Qian, QIN Yingyi, WU Cheng, HE Jia. Common statistical methods used in quasi-experimental studies and applications in medicine. Chinese Journal of Evidence-Based Medicine, 2022, 22(9): 1080-1084. doi: 10.7507/1672-2531.202204020 Copy

1.	Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med, 2000, 342(25): 1887-1892.
2.	窦亚兰, 戴江红, 黄爱龙. 准试验研究. 中华流行病学杂志, 2015, 36(9): 1018-1019.
3.	Ashenfelter OC, Card D. Using the longitudinal structure of earnings to estimate the effect of training programs. Mass. , USA: National Bureau of Economic Research Cambridge, 1984.
4.	Athey S, Imbens GW. Identification and inference in nonlinear difference-in-differences models. Econometrica, 2006, 74(2): 431-497.
5.	Dimick JB, Ryan AM. Methods for evaluating changes in health care policy: the difference-in-differences approach. JAMA, 2014, 312(22): 2401-2402.
6.	刘小宁, 高文龙, 颜虹. 双重差分模型在社区干预研究效果评价中的应用. 中国卫生统计, 2013, 30(1): 21-22.
7.	沈敏学, 胡明, 曾娜, 等. 双重差分模型在医学研究中的应用. 中国卫生统计, 2015, 32(3): 528-531.
8.	赵炜, 赵焕虎, 牟菲, 等. 运用双重差分模型评价社区跌倒预防的干预效果. 中国妇幼卫生杂志, 2018, 9(1): 18-21.
9.	侯艳杰, 王瑜, 颜诗源, 等. 长期护理保险对中老年人医疗服务利用, 医疗负担及健康的影响—基于双重差分法的实证研究. 中国卫生政策研究, 2021, 14(9): 35-40.
10.	Michael YL, Wu C, Pan K, et al. Postmenopausal breast cancer and physical function change: a difference-in-differences analysis. J Am Geriatr Soc, 2020, 68(5): 1029-1036.
11.	Wright PG. Tariff on animal and vegetable oils. New York: Macmillan Company, 1928.
12.	Maciejewski ML, Brookhart MA. Using instrumental variables to address bias from unobserved confounders. JAMA, 2019, 321(21): 2124-2125.
13.	Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol, 2000, 29(4): 722-729.
14.	Martens EP, Pestman WR, de Boer A, et al. Instrumental variables: application and limitations. Epidemiology, 2006, 17(3): 260-267.
15.	Markovitz AA, Mack JA, Nallamothu BK, et al. Incremental effects of antihypertensive drugs: instrumental variable analysis. BMJ, 2017, 359: j5542.
16.	Chhabra KR, Telem DA, Chao GF, et al. Comparative safety of sleeve gastrectomy and gastric bypass: an instrumental variables approach. Ann Surg, 2022, 275(3): 539-545.
17.	Werner RM, Coe NB, Qi M, et al. Patient outcomes after hospital discharge to home with home health care vs to a skilled nursing facility. JAMA Intern Med, 2019, 179(5): 617-623.
18.	高雪, 薛付忠, 黄丽红, 等. 孟德尔随机化模型及其规范化应用的统计学共识. 中国卫生统计, 2021, 38(3): 471-475.
19.	Hartwig FP, Borges MC, Horta BL, et al. Inflammatory biomarkers and risk of schizophrenia: a 2-sample mendelian randomization study. JAMA Psychiatry, 2017, 74(12): 1226-1233.
20.	Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference. Stat Med, 2014, 33(13): 2297-2340.
21.	Thistlethwaite DL, Campbell DT. Regression-discontinuity analysis: an alternative to the ex post facto experiment. J Edu Psychol, 1960, 51(6): 309.
22.	Venkataramani AS, Bor J, Jena AB. Regression discontinuity designs in healthcare research. BMJ, 2016, 352: i1216.
23.	郭昭艳, 刘莉, 余方方, 等. 断点回归设计在流行病学研究中的应用. 中华预防医学杂志, 2021, 55(9): 1168-1172.
24.	王健, 冷安丽, 唐彬. 断点回归设计在卫生经济领域的应用与进展. 中国卫生经济, 2018, 37(7): 8-11.
25.	赵西亮. 基本有用的计量经济学. 北京: 北京大学出版社, 2017: 186-194.
26.	Bor J, Moscoe E, Mutevedzi P, et al. Regression discontinuity designs in epidemiology: causal inference without randomized trials. Epidemiology, 2014, 25(5): 729-737.
27.	Tennant P, Doxford-Hook E, Flynn L, et al. Fasting plasma glucose, diagnosis of gestational diabetes and the risk of large for gestational age: a regression discontinuity analysis of routine data. BJOG, 2022, 129(1): 82-89.
28.	Scott L, Redaniel MT, Booker M, et al. Regression discontinuity analysis for pharmacovigilance: statin example reflected trial findings showing little evidence of harm. J Clin Epidemiol, 2022, 141: 121-131.
29.	Xiong X, Li R, Yang H. How the spouse's retirement affects the cognitive health of individuals in china: a fresh evidence from the perspective of social interaction. Front Public Health, 2021, 9: 796775.
30.	Box GE, Tiao GC. Intervention analysis with applications to economic and environmental problems. J Am Stat Associat, 1975, 70(349): 70-79.
31.	Penfold RB, Zhang F. Use of interrupted time series analysis in evaluating health care quality improvements. Acad Pediatr, 2013, 13(6 Suppl): S38-S44.
32.	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.
33.	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.
34.	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.
35.	王飞, 汤少梁, 赵琨, 等. 应用间断时间序列评价某县级公立医院医药价格改革效果. 中国卫生统计, 2016, 33(1): 78-80.
36.	Findlay BL, Britton CJ, Glasgow AE, et al. Long-term success with diminished opioid prescribing after implementation of standardized postoperative opioid prescribing guidelines: an interrupted time series analysis. Mayo Clin Proc, 2021, 96(5): 1135-1146.
37.	Clavería A, Delgado-Martín MV, Goicoechea-Castaño A, et al. Interrupted time series analysis of pediatric infectious diseases and the consumption of antibiotics in an atlantic european region during the SARS-CoV-2 pandemic. Antibiotics (Basel), 2022, 11(2): 264.
38.	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.
39.	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, 53(8): 858-864.
40.	Hendrickse J, Yeaton WH. An empirical validation of the regression point displacement design using within-study comparison logic: emerging possibilities and cautions. Eval Rev, 2021, 45(6): 279-308.
41.	Bonander C, Humphreys D, Degli Esposti M. Synthetic control methods for the evaluation of single-unit interventions in epidemiology: a tutorial. Am J Epidemiol, 2021, 190(12): 2700-2711.
42.	Harris AD, Lautenbach E, Perencevich E. A systematic review of quasi-experimental study designs in the fields of infection control and antibiotic resistance. Clin Infect Dis, 2005, 41(1): 77-82.
43.	Lopez Bernal JA, Andrews N, Amirthalingam G. The use of quasi-experimental designs for vaccine evaluation. Clin Infect Dis, 2019, 68(10): 1769-1776.
44.	Liu T, Ungar L, Kording K. Quantifying causality in data science with quasi-experiments. Nat Comput Sci, 2021, 1(1): 24-32.
45.	黄丽红, 魏永越, 陈峰. 如何控制观察性疗效比较研究中的混杂因素: (二)未知或未测量混杂因素的统计学分析方法. 中华流行病学杂志, 2019, 40(11): 1450-1455.

1. Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med, 2000, 342(25): 1887-1892.
2. 窦亚兰, 戴江红, 黄爱龙. 准试验研究. 中华流行病学杂志, 2015, 36(9): 1018-1019.
3. Ashenfelter OC, Card D. Using the longitudinal structure of earnings to estimate the effect of training programs. Mass. , USA: National Bureau of Economic Research Cambridge, 1984.
4. Athey S, Imbens GW. Identification and inference in nonlinear difference-in-differences models. Econometrica, 2006, 74(2): 431-497.
5. Dimick JB, Ryan AM. Methods for evaluating changes in health care policy: the difference-in-differences approach. JAMA, 2014, 312(22): 2401-2402.
6. 刘小宁, 高文龙, 颜虹. 双重差分模型在社区干预研究效果评价中的应用. 中国卫生统计, 2013, 30(1): 21-22.
7. 沈敏学, 胡明, 曾娜, 等. 双重差分模型在医学研究中的应用. 中国卫生统计, 2015, 32(3): 528-531.
8. 赵炜, 赵焕虎, 牟菲, 等. 运用双重差分模型评价社区跌倒预防的干预效果. 中国妇幼卫生杂志, 2018, 9(1): 18-21.
9. 侯艳杰, 王瑜, 颜诗源, 等. 长期护理保险对中老年人医疗服务利用, 医疗负担及健康的影响—基于双重差分法的实证研究. 中国卫生政策研究, 2021, 14(9): 35-40.
10. Michael YL, Wu C, Pan K, et al. Postmenopausal breast cancer and physical function change: a difference-in-differences analysis. J Am Geriatr Soc, 2020, 68(5): 1029-1036.
11. Wright PG. Tariff on animal and vegetable oils. New York: Macmillan Company, 1928.
12. Maciejewski ML, Brookhart MA. Using instrumental variables to address bias from unobserved confounders. JAMA, 2019, 321(21): 2124-2125.
13. Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol, 2000, 29(4): 722-729.
14. Martens EP, Pestman WR, de Boer A, et al. Instrumental variables: application and limitations. Epidemiology, 2006, 17(3): 260-267.
15. Markovitz AA, Mack JA, Nallamothu BK, et al. Incremental effects of antihypertensive drugs: instrumental variable analysis. BMJ, 2017, 359: j5542.
16. Chhabra KR, Telem DA, Chao GF, et al. Comparative safety of sleeve gastrectomy and gastric bypass: an instrumental variables approach. Ann Surg, 2022, 275(3): 539-545.
17. Werner RM, Coe NB, Qi M, et al. Patient outcomes after hospital discharge to home with home health care vs to a skilled nursing facility. JAMA Intern Med, 2019, 179(5): 617-623.
18. 高雪, 薛付忠, 黄丽红, 等. 孟德尔随机化模型及其规范化应用的统计学共识. 中国卫生统计, 2021, 38(3): 471-475.
19. Hartwig FP, Borges MC, Horta BL, et al. Inflammatory biomarkers and risk of schizophrenia: a 2-sample mendelian randomization study. JAMA Psychiatry, 2017, 74(12): 1226-1233.
20. Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference. Stat Med, 2014, 33(13): 2297-2340.
21. Thistlethwaite DL, Campbell DT. Regression-discontinuity analysis: an alternative to the ex post facto experiment. J Edu Psychol, 1960, 51(6): 309.
22. Venkataramani AS, Bor J, Jena AB. Regression discontinuity designs in healthcare research. BMJ, 2016, 352: i1216.
23. 郭昭艳, 刘莉, 余方方, 等. 断点回归设计在流行病学研究中的应用. 中华预防医学杂志, 2021, 55(9): 1168-1172.
24. 王健, 冷安丽, 唐彬. 断点回归设计在卫生经济领域的应用与进展. 中国卫生经济, 2018, 37(7): 8-11.
25. 赵西亮. 基本有用的计量经济学. 北京: 北京大学出版社, 2017: 186-194.
26. Bor J, Moscoe E, Mutevedzi P, et al. Regression discontinuity designs in epidemiology: causal inference without randomized trials. Epidemiology, 2014, 25(5): 729-737.
27. Tennant P, Doxford-Hook E, Flynn L, et al. Fasting plasma glucose, diagnosis of gestational diabetes and the risk of large for gestational age: a regression discontinuity analysis of routine data. BJOG, 2022, 129(1): 82-89.
28. Scott L, Redaniel MT, Booker M, et al. Regression discontinuity analysis for pharmacovigilance: statin example reflected trial findings showing little evidence of harm. J Clin Epidemiol, 2022, 141: 121-131.
29. Xiong X, Li R, Yang H. How the spouse's retirement affects the cognitive health of individuals in china: a fresh evidence from the perspective of social interaction. Front Public Health, 2021, 9: 796775.
30. Box GE, Tiao GC. Intervention analysis with applications to economic and environmental problems. J Am Stat Associat, 1975, 70(349): 70-79.
31. Penfold RB, Zhang F. Use of interrupted time series analysis in evaluating health care quality improvements. Acad Pediatr, 2013, 13(6 Suppl): S38-S44.
32. 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.
33. 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.
34. 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.
35. 王飞, 汤少梁, 赵琨, 等. 应用间断时间序列评价某县级公立医院医药价格改革效果. 中国卫生统计, 2016, 33(1): 78-80.
36. Findlay BL, Britton CJ, Glasgow AE, et al. Long-term success with diminished opioid prescribing after implementation of standardized postoperative opioid prescribing guidelines: an interrupted time series analysis. Mayo Clin Proc, 2021, 96(5): 1135-1146.
37. Clavería A, Delgado-Martín MV, Goicoechea-Castaño A, et al. Interrupted time series analysis of pediatric infectious diseases and the consumption of antibiotics in an atlantic european region during the SARS-CoV-2 pandemic. Antibiotics (Basel), 2022, 11(2): 264.
38. 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.
39. 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, 53(8): 858-864.
40. Hendrickse J, Yeaton WH. An empirical validation of the regression point displacement design using within-study comparison logic: emerging possibilities and cautions. Eval Rev, 2021, 45(6): 279-308.
41. Bonander C, Humphreys D, Degli Esposti M. Synthetic control methods for the evaluation of single-unit interventions in epidemiology: a tutorial. Am J Epidemiol, 2021, 190(12): 2700-2711.
42. Harris AD, Lautenbach E, Perencevich E. A systematic review of quasi-experimental study designs in the fields of infection control and antibiotic resistance. Clin Infect Dis, 2005, 41(1): 77-82.
43. Lopez Bernal JA, Andrews N, Amirthalingam G. The use of quasi-experimental designs for vaccine evaluation. Clin Infect Dis, 2019, 68(10): 1769-1776.
44. Liu T, Ungar L, Kording K. Quantifying causality in data science with quasi-experiments. Nat Comput Sci, 2021, 1(1): 24-32.
45. 黄丽红, 魏永越, 陈峰. 如何控制观察性疗效比较研究中的混杂因素: (二)未知或未测量混杂因素的统计学分析方法. 中华流行病学杂志, 2019, 40(11): 1450-1455.

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Common statistical methods used in quasi-experimental studies and applications in medicine

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