Introduction and application of an auxiliary implementation tool (CERBOT) for target trial emulation study_Chinese Journal of Evidence-Based Medicine

Authors：

 LU Cuncun ¹ , KE Lixin ² , WANG Ziyi ³ , CHEN Zijia ¹ ,  WANG Zhifei ¹

1. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, P. R. China;
2. Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen 9713GZ, Netherlands;
3. Evidence Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou 730000, P. R. China;

Corresponding author：

LU Cuncun, Email: cuncunlu2017@163.com; WANG Zhifei, Email: wzhftcm@163.com

Keywords：

Target trial; Causal inference; Comparative effectiveness research; Real-world data

DOI：

10.7507/1672-2531.202307121

Video：

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The rapid advancement of causal inference is driving a paradigm shift across various disciplines. "Target trial emulation" has emerged as an exceptionally promising framework for observational real-world studies, attracting substantial attention from medical scholars and regulatory agencies worldwide. This article aims to provide an introduction to CERBOT, an online tool that assists in implementing target trial emulation studies, while highlighting the advancements in this domain. Additionally, the article provides an illustrative example to elucidate the operational process of CERBOT. The objectives are to support domestic researchers in conducting target trial emulation studies and enhance the quality of real-world studies in the domestic medical field, as well as improve the medical service level in clinical practice.

Citation： LU Cuncun, KE Lixin, WANG Ziyi, CHEN Zijia, WANG Zhifei. Introduction and application of an auxiliary implementation tool (CERBOT) for target trial emulation study. Chinese Journal of Evidence-Based Medicine, 2024, 24(2): 237-242. doi: 10.7507/1672-2531.202307121 Copy

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3.	Baumfeld Andre E, Reynolds R, Caubel P, et al. Trial designs using real-world data: the changing landscape of the regulatory approval process. Pharmacoepidemiol Drug Saf, 2020, 29(10): 1201-1212.
4.	卢存存, 陈子佳, 张强, 等. 基于真实世界数据的目标试验模拟研究: 现状与展望. 中国循证医学杂志, 2023, 23(4): 492-496.
5.	Fu EL. Target trial emulation to improve causal inference from observational data: what, why, and how. J Am Soc Nephrol, 2023, 34(8): 1305-1314.
6.	Gehrmann J, Herczog E, Decker S, et al. What prevents us from reusing medical real-world data in research. Sci Data, 2023, 10(1): 459.
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9.	He W, Fang Y, Wang H. Real-world evidence in medical product development. Cham: Springer, 2023.
10.	Bykov K, Patorno E, D'Andrea E, et al. Prevalence of avoidable and bias-inflicting methodological pitfalls in real-world studies of medication safety and effectiveness. Clin Pharmacol Ther, 2022, 111(1): 209-217.
11.	Zhao R, Zhang W, Zhang Z, et al. Evaluation of reporting quality of cohort studies using real-world data based on RECORD: systematic review. BMC Med Res Methodol, 2023, 23(1): 152.
12.	Wang W, Liu M, He Q, et al. Data source profile reporting by studies that use routinely collected health data to explore the effects of drug treatment. BMC Med Res Methodol, 2023, 23(1): 95.
13.	Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol, 2016, 183(8): 758-764.
14.	St MA, Robins JM. Using Big Data et al. Methodological considerations when analysing and interpreting real-world data. Rheumatology (Oxford), 2020, 59(1): 14-25.
15.	Hernán MA. How to estimate the effect of treatment duration on survival outcomes using observational data. BMJ, 2018, 360: k182.
16.	Mlcoch T, Hrnciarova T, Tuzil J, et al. Propensity score weighting using overlap weights: a new method applied to regorafenib clinical data and a cost-effectiveness analysis. Value Health, 2019, 22(12): 1370-1377.
17.	Schneeweiss S, Patorno E. Conducting real-world evidence studies on the clinical outcomes of diabetes treatments. Endocr Rev, 2021, 42(5): 658-690.
18.	赵骏, 王骏. 应用模拟目标临床试验概念设计观察性研究时的若干考虑. 中国新药杂志, 2022, 31(18): 1801-1803.
19.	De Stavola BL, Gomes M, Katsoulis M. Transparency and rigor: target trial emulation aims to achieve both. Epidemiology, 2023, 34(5): 624-626.
20.	石舒原, 刘佐相, 赵厚宇, 等. 真实世界证据与随机对照试验: RCTDUPLICATE项目启动、实施、进展解读与启示(一). 中华流行病学杂志, 2022, 43(11): 1828-1834.
21.	Wang SV, Schneeweiss S, , et al. Emulation of randomized clinical trials with nonrandomized database analyses: results of 32 clinical trials. JAMA, 2023, 329(16): 1376-1385.
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25.	Pilleron S, Maringe C, Morris EJA, et al. Immortal-time bias in older vs younger age groups: a simulation study with application to a population-based cohort of patients with colon cancer. Br J Cancer, 2023, 128(8): 1521-1528.
26.	Ho M, Gruber S, Fang Y, et al. Examples of applying RWE causal-inference roadmap to clinical studies. Stat Biopharm Res, 2023, 15(3): 1-14.
27.	Wang SV, Sreedhara SK, Bessette LG, et al. Understanding variation in the results of real-world evidence studies that seem to address the same question. J Clin Epidemiol, 2022, 151: 161-170.
28.	Yu OHY, Suissa S. Metformin and cancer: solutions to a real-world evidence failure. Diabetes Care, 2023, 46(5): 904-912.
29.	Koro M, Wolgemuth J, Trinh E. Reducing methodological footprints in qualitative research. Qualitative Inquiry, 2023.
30.	Quann N, Burns S, Hull KL, et al. Reducing the carbon footprint of research: experience from the NightLife study. BMJ Open, 2023, 13(4): e070200.
31.	Saldarriaga EM, Hauber B, Carlson JJ, et al. Assessing payers' preferences for real-world evidence in the united states: a discrete choice experiment. Value Health, 2022, 25(3): 443-450.
32.	Wolfson J, Venkatasubramaniam A. Best (but often forgotten) statistical practices: measuring real-world intervention effectiveness using electronic health data. Am J Clin Nutr, 2023, 118(1): 13-22.
33.	Hulme WJ, Williamson E, Horne EMF, et al. Challenges in estimating the effectiveness of COVID-19 vaccination using observational data. Ann Intern Med, 2023, 176(5): 685-693.
34.	Moler-Zapata S, Hutchings A, O'Neill S, et al. Emulating target trials with real-world data to inform health technology assessment: findings and lessons from an application to emergency surgery. Value Health, 2023, 26(8): 1164-1174.
35.	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, 2023.

1. 丁鹏. 因果推断—现代统计的思想飞跃. 数学文化, 2021, 12(2): 51-67.
2. Hernán MA, Robins JM. Causal inference: what if. Boca Raton: Chapman & Hall, 2020.
3. Baumfeld Andre E, Reynolds R, Caubel P, et al. Trial designs using real-world data: the changing landscape of the regulatory approval process. Pharmacoepidemiol Drug Saf, 2020, 29(10): 1201-1212.
4. 卢存存, 陈子佳, 张强, 等. 基于真实世界数据的目标试验模拟研究: 现状与展望. 中国循证医学杂志, 2023, 23(4): 492-496.
5. Fu EL. Target trial emulation to improve causal inference from observational data: what, why, and how. J Am Soc Nephrol, 2023, 34(8): 1305-1314.
6. Gehrmann J, Herczog E, Decker S, et al. What prevents us from reusing medical real-world data in research. Sci Data, 2023, 10(1): 459.
7. Taylor SP, Kowalkowski MA, Admon AJ. Timing is everything. the importance of alignment of time anchors for observational causal inference research. Ann Am Thorac Soc, 2021, 18(5): 769-772.
8. Schneeweiss S, Rassen JA, Brown JS, et al. Graphical depiction of longitudinal study designs in health care databases. Ann Intern Med, 2019, 170(6): 398-406.
9. He W, Fang Y, Wang H. Real-world evidence in medical product development. Cham: Springer, 2023.
10. Bykov K, Patorno E, D'Andrea E, et al. Prevalence of avoidable and bias-inflicting methodological pitfalls in real-world studies of medication safety and effectiveness. Clin Pharmacol Ther, 2022, 111(1): 209-217.
11. Zhao R, Zhang W, Zhang Z, et al. Evaluation of reporting quality of cohort studies using real-world data based on RECORD: systematic review. BMC Med Res Methodol, 2023, 23(1): 152.
12. Wang W, Liu M, He Q, et al. Data source profile reporting by studies that use routinely collected health data to explore the effects of drug treatment. BMC Med Res Methodol, 2023, 23(1): 95.
13. Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol, 2016, 183(8): 758-764.
14. St MA, Robins JM. Using Big Data et al. Methodological considerations when analysing and interpreting real-world data. Rheumatology (Oxford), 2020, 59(1): 14-25.
15. Hernán MA. How to estimate the effect of treatment duration on survival outcomes using observational data. BMJ, 2018, 360: k182.
16. Mlcoch T, Hrnciarova T, Tuzil J, et al. Propensity score weighting using overlap weights: a new method applied to regorafenib clinical data and a cost-effectiveness analysis. Value Health, 2019, 22(12): 1370-1377.
17. Schneeweiss S, Patorno E. Conducting real-world evidence studies on the clinical outcomes of diabetes treatments. Endocr Rev, 2021, 42(5): 658-690.
18. 赵骏, 王骏. 应用模拟目标临床试验概念设计观察性研究时的若干考虑. 中国新药杂志, 2022, 31(18): 1801-1803.
19. De Stavola BL, Gomes M, Katsoulis M. Transparency and rigor: target trial emulation aims to achieve both. Epidemiology, 2023, 34(5): 624-626.
20. 石舒原, 刘佐相, 赵厚宇, 等. 真实世界证据与随机对照试验: RCTDUPLICATE项目启动、实施、进展解读与启示(一). 中华流行病学杂志, 2022, 43(11): 1828-1834.
21. Wang SV, Schneeweiss S, , et al. Emulation of randomized clinical trials with nonrandomized database analyses: results of 32 clinical trials. JAMA, 2023, 329(16): 1376-1385.
22. 中国国家药监局药品审评中心(CDE). 《药物真实世界研究设计与方案框架指导原则(试行)》. 2023.
23. Mohyuddin GR, Prasad V. Detecting selection bias in observational studies-when interventions work too fast. JAMA Intern Med, 2023, 183(9): 897-898.
24. Zhang Y, Thamer M, Kshirsagar O, et al. Comparative effectiveness research based on observational data to emulate a target trial. Medical Technology and Practice Patterns Institute, 2019.
25. Pilleron S, Maringe C, Morris EJA, et al. Immortal-time bias in older vs younger age groups: a simulation study with application to a population-based cohort of patients with colon cancer. Br J Cancer, 2023, 128(8): 1521-1528.
26. Ho M, Gruber S, Fang Y, et al. Examples of applying RWE causal-inference roadmap to clinical studies. Stat Biopharm Res, 2023, 15(3): 1-14.
27. Wang SV, Sreedhara SK, Bessette LG, et al. Understanding variation in the results of real-world evidence studies that seem to address the same question. J Clin Epidemiol, 2022, 151: 161-170.
28. Yu OHY, Suissa S. Metformin and cancer: solutions to a real-world evidence failure. Diabetes Care, 2023, 46(5): 904-912.
29. Koro M, Wolgemuth J, Trinh E. Reducing methodological footprints in qualitative research. Qualitative Inquiry, 2023.
30. Quann N, Burns S, Hull KL, et al. Reducing the carbon footprint of research: experience from the NightLife study. BMJ Open, 2023, 13(4): e070200.
31. Saldarriaga EM, Hauber B, Carlson JJ, et al. Assessing payers' preferences for real-world evidence in the united states: a discrete choice experiment. Value Health, 2022, 25(3): 443-450.
32. Wolfson J, Venkatasubramaniam A. Best (but often forgotten) statistical practices: measuring real-world intervention effectiveness using electronic health data. Am J Clin Nutr, 2023, 118(1): 13-22.
33. Hulme WJ, Williamson E, Horne EMF, et al. Challenges in estimating the effectiveness of COVID-19 vaccination using observational data. Ann Intern Med, 2023, 176(5): 685-693.
34. Moler-Zapata S, Hutchings A, O'Neill S, et al. Emulating target trials with real-world data to inform health technology assessment: findings and lessons from an application to emergency surgery. Value Health, 2023, 26(8): 1164-1174.
35. 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, 2023.

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