Application and practice of active comparator - new user design in real-world study_Chinese Journal of Evidence-Based Medicine

Authors：

WANG Xin ¹ , CAO Xue ¹ , TIAN Yukun ¹ , LONG Zhengli ² , SUN Ruijie ² , LIU Xinyi ¹ ,  LIU Jia ¹

1. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, P. R. China;
2. School of Acupuncture-Moxibustion and Tuina, Shaanxi University of Chinese Medicine, Xian 710000, P. R. China;

Corresponding author：

LIU Jia, Email: Marie_liujia@163.com

Keywords：

Real-world data; Active comparator; New-user design; Bias control

DOI：

10.7507/1672-2531.202410027

Video：

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Abstract Full text Figures/Tables Video References Cited by

The active comparator, new user (ACNU) design is an important design developed under the concept of the target simulation experimental framework. It aims to reduce indication confounding, immortal time bias, prevalence-incidence bias, and other unmeasured confounders by simulating head-to-head randomized controlled trials. It is widely applied in scenarios such as comparing the efficacy of newly marketed drugs with existing standard treatments, evaluating drug safety and adherence, exploring drug repurposing, and optimizing algorithms for processing medical big data. This article introduces the application and practice of the ACNU design in real-world data research from aspects such as concept, development, advantages and disadvantages, and implementation points, and also presents an outlook on its application in the field of traditional Chinese medicine. It is believed that with the progress in understanding the design of observational studies of real-world data, the ACNU design is expected to be more widely applied and provide new ideas for researchers' scientific research designs.

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2.	Kyriacou DN, Lewis RJ. Confounding by indication in clinical research. JAMA, 2016, 316(17): 1818-1819.
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6.	Schneeweiss S, Patorno E. Conducting real-world evidence studies on the clinical outcomes of diabetes treatments. Endocr Rev, 2021, 42(5): 658-690.
7.	姜棋予, 孙丽君, 毕京峰. 观察性研究的敏感性分析: “E-value”解读. 中国医药导报. 2020, 17(11): 181-185, 192.
8.	Psaty BM, Siscovick DS. Minimizing bias due to confounding by indication in comparative effectiveness research: the importance of restriction. JAMA, 2010, 304(8): 897-898.
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12.	Yu OHY, Suissa S. Metformin and cancer: solutions to a real-world evidence failure. Diabetes Care, 2023, 46(5): 904-912.
13.	Manson JE, Hsia J, Johnson KC, et al. Estrogen plus progestin and the risk of coronary heart disease. N Engl J Med, 2003, 349(6): 523-534.
14.	Grodstein F, Stampfer MJ, Manson JE, et al. Postmenopausal estrogen and progestin use and the risk of cardiovascular disease. N Engl J Med, 1996, 335(7): 453-461.
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16.	Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol, 2003, 158(9): 915-920.
17.	Her QL, Rouette J, Young JC, et al. Core concepts in pharmacoepidemiology: new-user designs. Pharmacoepidemiol Drug Saf, 2024, 33(12): e70048.
18.	彭晓霞, 舒啸尘, 谭婧, 等. 基于真实世界数据评价治疗结局的观察性研究设计技术规范. 中国循证医学杂志. 2019, 19(7): 779-786.
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23.	Franklin JM, Patorno E, Desai RJ, et al. Emulating randomized clinical trials with nonrandomized real-world evidence studies: first results from the RCT DUPLICATE Initiative. Circulation, 2021, 143(10): 1002-1013.
24.	Suissa S, Moodie EE, Dell'Aniello S. Prevalent new-user cohort designs for comparative drug effect studies by time-conditional propensity scores. Pharmacoepidemiol Drug Saf, 2017, 26(4): 459-468.
25.	Tazare J, Gibbons DC, Bokern M, et al. Prevalent new user designs: a literature review of current implementation practice. Pharmacoepidemiol Drug Saf, 2023, 32(11): 1252-1260.
26.	Smith R, Villanueva G, Probyn K, et al. Accuracy of measures for antiretroviral adherence in people living with HIV. Cochrane Database Syst Rev, 2022, (7): CD013080.
27.	VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the e-value. Ann Intern Med, 2017, 167(4): 268-274.
28.	Haneuse S, VanderWeele TJ, Arterburn D. Using the e-value to assess the potential effect of unmeasured confounding in observational studies. JAMA, 2019, 321(6): 602-603.
29.	Rachel S, Til S. Core concepts in pharmacoepidemiology: confounding by indication and the role of active comparators. Pharmacoepidemiol Drug Saf, 2022, 31(3): 261-269.
30.	Patil T, Ali S, Eppes D, et al. Evaluating the safety and effectiveness of direct oral anticoagulants compared with warfarin in very elderly patients with atrial fibrillation with and without low bodyweight. J Thromb Haemost, 2024, 22(11): 3107-3124.
31.	Moriarty F, Thompson W, Boland F. Methods for evaluating the benefit and harms of deprescribing in observational research using routinely collected data. Res Social Adm Pharm, 2022, 18(2): 2269-2275.
32.	Temple R, Ellenberg SS. Placebo-controlled trials and active-control trials in the evaluation of new treatments. Part 1: ethical and scientific issues. Ann Intern Med, 2000, 133(6): 455-463.
33.	Stürmer T, Wang T, Golightly YM, et al. Methodological considerations when analysing and interpreting real-world data. Rheumatology (Oxford), 2020, 59(1): 14-25.
34.	Anson M, Henney AE, Broadwell N, et al. Incidence of new onset type 2 diabetes in adults living with obesity treated with tirzepatide or semaglutide: real world evidence from an international retrospective cohort study. EClinicalMedicine, 2024, 75: 102777.
35.	Kanwal F, Kramer JR, Li L, et al. GLP-1 receptor agonists and risk for cirrhosis and related complications in patients with metabolic dysfunction-associated steatotic liver disease. JAMA Intern Med, 2024, 184(11): 1314-1323.
36.	Parimi M, Svedsater H, Ann Q, et al. Persistence and adherence to ICS/LABA drugs in UK patients with asthma: a retrospective new-user cohort study. Adv Ther, 2020, 37(6): 2916-2931.
37.	Tan GSQ, Maro JC, Wang SV, et al. Tree-based scan statistics to generate drug repurposing hypotheses: a test case using sodium-glucose cotransporter-2 inhibitors. Am J Epidemiol, 2024: kwae355.
38.	Wang T, Pate V, Wyss R, et al. High-dimensional iterative causal forest (hdiCF) for subgroup identification using health care claims data. Am J Epidemiol, 2024: kwae322.
39.	Abdelaziz AI, Hanson KA, Gaber CE, et al. Optimizing large real-world data analysis with parquet files in R: a step-by-step tutorial. Pharmacoepidemiol Drug Saf, 2024, 33(3): e5728.
40.	Mansi IA, Chansard M, Lingvay I, et al. Association of statin therapy initiation with diabetes progression: a retrospective matched-cohort study. JAMA Intern Med, 2021, 181(12): 1562-1574.
41.	周雪忠, 王世华, 张迪, 等. 构建中医药特色真实世界临床研究新模式的实践与思考. 科技导报. 2023, 41(14): 22-31.

1. Cohen JB, D'Agostino McGowan L, Jensen ET, et al. Evaluating sources of bias in observational studies of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker use during COVID-19: beyond confounding. J Hypertens, 2021, 39(4): 795-805.
2. Kyriacou DN, Lewis RJ. Confounding by indication in clinical research. JAMA, 2016, 316(17): 1818-1819.
3. 卢存存, 陈子佳, 王志飞. 基于真实世界数据的观察性因果推断研究新框架(目标试验模拟)及其在中医药领域中的应用展望. 协和医学杂志. 2024, 15(2): 422-428.
4. Yoshida K, Solomon DH, Kim SC. Active-comparator design and new-user design in observational studies. Nat Rev Rheumatol, 2015, 11(7): 437-441.
5. Nguyen VT, Engleton M, Davison M, et al. Risk of bias in observational studies using routinely collected data of comparative effectiveness research: a meta-research study. BMC Med, 2021, 19(1): 279.
6. Schneeweiss S, Patorno E. Conducting real-world evidence studies on the clinical outcomes of diabetes treatments. Endocr Rev, 2021, 42(5): 658-690.
7. 姜棋予, 孙丽君, 毕京峰. 观察性研究的敏感性分析: “E-value”解读. 中国医药导报. 2020, 17(11): 181-185, 192.
8. Psaty BM, Siscovick DS. Minimizing bias due to confounding by indication in comparative effectiveness research: the importance of restriction. JAMA, 2010, 304(8): 897-898.
9. Østergaard SD. Beware of confounding by indication in pharmacoepidemiological studies. Nord J Psychiatry, 2023, 77(6): 521-522.
10. Hernán MA, Alonso A, Logan R, et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology, 2008, 19(6): 766-779.
11. Tarhini Z, Manceur K, Magne J, et al. The effect of metformin on the survival of colorectal cancer patients with type 2 diabetes mellitus. Sci Rep, 2022, 12(1): 12374.
12. Yu OHY, Suissa S. Metformin and cancer: solutions to a real-world evidence failure. Diabetes Care, 2023, 46(5): 904-912.
13. Manson JE, Hsia J, Johnson KC, et al. Estrogen plus progestin and the risk of coronary heart disease. N Engl J Med, 2003, 349(6): 523-534.
14. Grodstein F, Stampfer MJ, Manson JE, et al. Postmenopausal estrogen and progestin use and the risk of cardiovascular disease. N Engl J Med, 1996, 335(7): 453-461.
15. Lund JL, Richardson DB, Stürmer T. The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application. Curr Epidemiol Rep, 2015, 2(4): 221-228.
16. Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol, 2003, 158(9): 915-920.
17. Her QL, Rouette J, Young JC, et al. Core concepts in pharmacoepidemiology: new-user designs. Pharmacoepidemiol Drug Saf, 2024, 33(12): e70048.
18. 彭晓霞, 舒啸尘, 谭婧, 等. 基于真实世界数据评价治疗结局的观察性研究设计技术规范. 中国循证医学杂志. 2019, 19(7): 779-786.
19. Feinstein AR. Clinical biostatistics. XI. Sources of 'chronology bias' in cohort statistics. Clin Pharmacol Ther, 1971, 12(5): 864-879.
20. Beecher HK. The powerful placebo. J Am Med Assoc, 1955, 159(17): 1602-1606.
21. Developing a protocol for observational comparative effectiveness research: a user's guide. Rockville (MD): 2013.
22. D'Arcy M, Stürmer T, Lund JL. The importance and implications of comparator selection in pharmacoepidemiologic research. Curr Epidemiol Rep, 2018, 5(3): 272-283.
23. Franklin JM, Patorno E, Desai RJ, et al. Emulating randomized clinical trials with nonrandomized real-world evidence studies: first results from the RCT DUPLICATE Initiative. Circulation, 2021, 143(10): 1002-1013.
24. Suissa S, Moodie EE, Dell'Aniello S. Prevalent new-user cohort designs for comparative drug effect studies by time-conditional propensity scores. Pharmacoepidemiol Drug Saf, 2017, 26(4): 459-468.
25. Tazare J, Gibbons DC, Bokern M, et al. Prevalent new user designs: a literature review of current implementation practice. Pharmacoepidemiol Drug Saf, 2023, 32(11): 1252-1260.
26. Smith R, Villanueva G, Probyn K, et al. Accuracy of measures for antiretroviral adherence in people living with HIV. Cochrane Database Syst Rev, 2022, (7): CD013080.
27. VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the e-value. Ann Intern Med, 2017, 167(4): 268-274.
28. Haneuse S, VanderWeele TJ, Arterburn D. Using the e-value to assess the potential effect of unmeasured confounding in observational studies. JAMA, 2019, 321(6): 602-603.
29. Rachel S, Til S. Core concepts in pharmacoepidemiology: confounding by indication and the role of active comparators. Pharmacoepidemiol Drug Saf, 2022, 31(3): 261-269.
30. Patil T, Ali S, Eppes D, et al. Evaluating the safety and effectiveness of direct oral anticoagulants compared with warfarin in very elderly patients with atrial fibrillation with and without low bodyweight. J Thromb Haemost, 2024, 22(11): 3107-3124.
31. Moriarty F, Thompson W, Boland F. Methods for evaluating the benefit and harms of deprescribing in observational research using routinely collected data. Res Social Adm Pharm, 2022, 18(2): 2269-2275.
32. Temple R, Ellenberg SS. Placebo-controlled trials and active-control trials in the evaluation of new treatments. Part 1: ethical and scientific issues. Ann Intern Med, 2000, 133(6): 455-463.
33. Stürmer T, Wang T, Golightly YM, et al. Methodological considerations when analysing and interpreting real-world data. Rheumatology (Oxford), 2020, 59(1): 14-25.
34. Anson M, Henney AE, Broadwell N, et al. Incidence of new onset type 2 diabetes in adults living with obesity treated with tirzepatide or semaglutide: real world evidence from an international retrospective cohort study. EClinicalMedicine, 2024, 75: 102777.
35. Kanwal F, Kramer JR, Li L, et al. GLP-1 receptor agonists and risk for cirrhosis and related complications in patients with metabolic dysfunction-associated steatotic liver disease. JAMA Intern Med, 2024, 184(11): 1314-1323.
36. Parimi M, Svedsater H, Ann Q, et al. Persistence and adherence to ICS/LABA drugs in UK patients with asthma: a retrospective new-user cohort study. Adv Ther, 2020, 37(6): 2916-2931.
37. Tan GSQ, Maro JC, Wang SV, et al. Tree-based scan statistics to generate drug repurposing hypotheses: a test case using sodium-glucose cotransporter-2 inhibitors. Am J Epidemiol, 2024: kwae355.
38. Wang T, Pate V, Wyss R, et al. High-dimensional iterative causal forest (hdiCF) for subgroup identification using health care claims data. Am J Epidemiol, 2024: kwae322.
39. Abdelaziz AI, Hanson KA, Gaber CE, et al. Optimizing large real-world data analysis with parquet files in R: a step-by-step tutorial. Pharmacoepidemiol Drug Saf, 2024, 33(3): e5728.
40. Mansi IA, Chansard M, Lingvay I, et al. Association of statin therapy initiation with diabetes progression: a retrospective matched-cohort study. JAMA Intern Med, 2021, 181(12): 1562-1574.
41. 周雪忠, 王世华, 张迪, 等. 构建中医药特色真实世界临床研究新模式的实践与思考. 科技导报. 2023, 41(14): 22-31.

Chinese Journal of Evidence-Based Medicine

Latest ArticlesApplication and practice of active comparator - new user design in real-world study

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