Target trial emulation study based on real world data: status quo and prospect_Chinese Journal of Evidence-Based Medicine

Authors：

 LU Cuncun , CHEN Zijia , ZHANG Qiang , QIAO Meng , LEI Chao , XIE Yanming , WANG Zhifei

Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, P. R. China;

Corresponding?author：

LU Cuncun, Email: wzhftcm@163.com

Keywords：

Target trial; Observational study; Real-world data; Casual inference

DOI：

10.7507/1672-2531.202209032

Video：

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Randomized controlled trials are considered as the gold standard for determining the causality, and are usually used to evaluate the efficacy and safety of medical interventions. However, in some cases it is not feasible to conduct a randomized controlled trial. In recent years, a framework called “target trial emulation study” has been formally established to guide the design and analysis of observational studies based on real-world data. This framework provides an effective method for causal inference based on observational studies. In order to facilitate domestic scholars to understand and apply the framework to solve related clinical problems, this article introduces it from the basic concept, framework structure and implementation steps, development status, and prospects.

Citation： LU Cuncun, CHEN Zijia, ZHANG Qiang, QIAO Meng, LEI Chao, XIE Yanming, WANG Zhifei. Target trial emulation study based on real world data: status quo and prospect. Chinese Journal of Evidence-Based Medicine, 2023, 23(4): 492-496. doi: 10.7507/1672-2531.202209032 Copy

1.	徐文濤, 于天琦, 游月媛, 等. 因果推斷: 不同目標效應量及其可比性問題. 中國循證醫學雜志, 2022, 22(4): 490-496.
2.	Hernán MA. Methods of public health research - strengthening causal inference from observational data. N Engl J Med, 2021, 385(15): 1345-1348.
3.	朱迪, 劉寶. 真實世界研究中的因果推斷及粗化精準匹配. 中國藥物經濟學, 2019, 14(10): 38-42,46.
4.	盧存存, 張強, 雷超, 等. 使用隊列和常規收集數據開展隨機對照試驗的報告清單(CONSORT-ROUTINE2021)解讀. 中國循證醫學雜志, 2022, 22(6): 731-738.
5.	Matthews AA, Danaei G, Islam N, et al. Target trial emulation: applying principles of randomised trials to observational studies. BMJ, 2022, 378: e071108.
6.	石舒原, 趙厚宇, 周慶欣, 等. 真實世界證據與隨機對照試驗: RCTDUPLICATE項目方法學介紹. 藥物流行病學雜志, 2020, 29(3): 198-205.
7.	Crown W, Dahabreh IJ, Li X, et al. Can observational analyses of routinely collected data emulate randomized trials. Design and feasibility of the observational patient evidence for regulatory approval science and understanding disease project. Value Health, 2023, 26(2): 176-184.
8.	任國強, 王于丹, 周云波. 科學研究中因果推斷的方法、應用與展望—以個體健康研究為例. 人口與經濟, 2022, (2): 1-25.
9.	Forbes SP, Dahabreh IJ. Benchmarking observational analyses against randomized trials: a review of studies assessing propensity score methods. J Gen Intern Med, 2020, 35(5): 1396-1404.
10.	彭曉霞, 舒嘯塵, 譚婧, 等. 基于真實世界數據評價治療結局的觀察性研究設計技術規范. 中國循證醫學雜志, 2019, 19(7): 779-786.
11.	Hernán MA, Sauer BC, Hernández-Díaz S, et al. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol, 2016, 79: 70-75.
12.	Fu EL, van Diepen M, Xu Y, et al. Pharmacoepidemiology for nephrologists (part 2): potential biases and how to overcome them. Clin Kidney J, 2020, 14(5): 1317-1326.
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.	Murray EJ, Marshall BDL, Buchanan AL. Emulating target trials to improve causal inference from agent-based models. Am J Epidemiol, 2021, 190(8): 1652-1658.
15.	Gomes M, Latimer N, Soares M, et al. Target trial emulation for transparent and robust estimation of treatment effects for health technology assessment using real-world data: opportunities and challenges. Pharmacoeconomics, 2022, 40(6): 577-586.
16.	Kutcher SA, Brophy JM, Banack HR, et al. Emulating a randomised controlled trial with observational data: an introduction to the target trial framework. Can J Cardiol, 2021, 37(9): 1365-1377.
17.	Dickerman BA, García-Albéniz X, Logan RW, et al. Avoidable flaws in observational analyses: an application to statins and cancer. Nat Med, 2019, 25(10): 1601-1606.
18.	Hulme WJ, Williamson EJ, Green ACA, et al. Comparative effectiveness of ChAdOx1 versus BNT162b2 covid-19 vaccines in health and social care workers in England: cohort study using OpenSAFELY. BMJ, 2022, 378: e068946.
19.	Kwee SA, Wong LL, Ludema C, et al. Target trial emulation: a design tool for cancer clinical trials. JCO Clin Cancer Inform, 2023, 7: e2200140.
20.	Pongiglione B, Torbica A. How real can we get in generating real world evidence. Exploring the opportunities of routinely collected administrative data for evaluation of medical devices. Health Econ, 2022, 31(Suppl 1): 25-43.
21.	Taipale H, Schneider-Thoma J, Pinzón-Espinosa J, et al. Representation and outcomes of individuals with schizophrenia seen in everyday practice who are ineligible for randomized clinical trials. JAMA Psychiatry, 2022, 79(3): 210-218.
22.	Zhao SS, Lyu H, Solomon DH, et al. Improving rheumatoid arthritis comparative effectiveness research through causal inference principles: systematic review using a target trial emulation framework. Ann Rheum Dis, 2020, 79(7): 883-890.
23.	Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ, 2016, 355: i4919.
24.	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.
25.	Bujkiewicz S, Singh J, Wheaton L, et al. Bridging disconnected networks of first and second lines of biologic therapies in rheumatoid arthritis with registry data: Bayesian evidence synthesis with target trial emulation. J Clin Epidemiol, 2022, 150: 171-178.
26.	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.
27.	Cro S, Kahan BC, Rehal S, et al. Evaluating how clear the questions being investigated in randomised trials are: systematic review of estimands. BMJ, 2022, 378: e070146.

1. 徐文濤, 于天琦, 游月媛, 等. 因果推斷: 不同目標效應量及其可比性問題. 中國循證醫學雜志, 2022, 22(4): 490-496.
2. Hernán MA. Methods of public health research - strengthening causal inference from observational data. N Engl J Med, 2021, 385(15): 1345-1348.
3. 朱迪, 劉寶. 真實世界研究中的因果推斷及粗化精準匹配. 中國藥物經濟學, 2019, 14(10): 38-42,46.
4. 盧存存, 張強, 雷超, 等. 使用隊列和常規收集數據開展隨機對照試驗的報告清單(CONSORT-ROUTINE2021)解讀. 中國循證醫學雜志, 2022, 22(6): 731-738.
5. Matthews AA, Danaei G, Islam N, et al. Target trial emulation: applying principles of randomised trials to observational studies. BMJ, 2022, 378: e071108.
6. 石舒原, 趙厚宇, 周慶欣, 等. 真實世界證據與隨機對照試驗: RCTDUPLICATE項目方法學介紹. 藥物流行病學雜志, 2020, 29(3): 198-205.
7. Crown W, Dahabreh IJ, Li X, et al. Can observational analyses of routinely collected data emulate randomized trials. Design and feasibility of the observational patient evidence for regulatory approval science and understanding disease project. Value Health, 2023, 26(2): 176-184.
8. 任國強, 王于丹, 周云波. 科學研究中因果推斷的方法、應用與展望—以個體健康研究為例. 人口與經濟, 2022, (2): 1-25.
9. Forbes SP, Dahabreh IJ. Benchmarking observational analyses against randomized trials: a review of studies assessing propensity score methods. J Gen Intern Med, 2020, 35(5): 1396-1404.
10. 彭曉霞, 舒嘯塵, 譚婧, 等. 基于真實世界數據評價治療結局的觀察性研究設計技術規范. 中國循證醫學雜志, 2019, 19(7): 779-786.
11. Hernán MA, Sauer BC, Hernández-Díaz S, et al. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol, 2016, 79: 70-75.
12. Fu EL, van Diepen M, Xu Y, et al. Pharmacoepidemiology for nephrologists (part 2): potential biases and how to overcome them. Clin Kidney J, 2020, 14(5): 1317-1326.
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. Murray EJ, Marshall BDL, Buchanan AL. Emulating target trials to improve causal inference from agent-based models. Am J Epidemiol, 2021, 190(8): 1652-1658.
15. Gomes M, Latimer N, Soares M, et al. Target trial emulation for transparent and robust estimation of treatment effects for health technology assessment using real-world data: opportunities and challenges. Pharmacoeconomics, 2022, 40(6): 577-586.
16. Kutcher SA, Brophy JM, Banack HR, et al. Emulating a randomised controlled trial with observational data: an introduction to the target trial framework. Can J Cardiol, 2021, 37(9): 1365-1377.
17. Dickerman BA, García-Albéniz X, Logan RW, et al. Avoidable flaws in observational analyses: an application to statins and cancer. Nat Med, 2019, 25(10): 1601-1606.
18. Hulme WJ, Williamson EJ, Green ACA, et al. Comparative effectiveness of ChAdOx1 versus BNT162b2 covid-19 vaccines in health and social care workers in England: cohort study using OpenSAFELY. BMJ, 2022, 378: e068946.
19. Kwee SA, Wong LL, Ludema C, et al. Target trial emulation: a design tool for cancer clinical trials. JCO Clin Cancer Inform, 2023, 7: e2200140.
20. Pongiglione B, Torbica A. How real can we get in generating real world evidence. Exploring the opportunities of routinely collected administrative data for evaluation of medical devices. Health Econ, 2022, 31(Suppl 1): 25-43.
21. Taipale H, Schneider-Thoma J, Pinzón-Espinosa J, et al. Representation and outcomes of individuals with schizophrenia seen in everyday practice who are ineligible for randomized clinical trials. JAMA Psychiatry, 2022, 79(3): 210-218.
22. Zhao SS, Lyu H, Solomon DH, et al. Improving rheumatoid arthritis comparative effectiveness research through causal inference principles: systematic review using a target trial emulation framework. Ann Rheum Dis, 2020, 79(7): 883-890.
23. Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ, 2016, 355: i4919.
24. 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.
25. Bujkiewicz S, Singh J, Wheaton L, et al. Bridging disconnected networks of first and second lines of biologic therapies in rheumatoid arthritis with registry data: Bayesian evidence synthesis with target trial emulation. J Clin Epidemiol, 2022, 150: 171-178.
26. 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.
27. Cro S, Kahan BC, Rehal S, et al. Evaluating how clear the questions being investigated in randomised trials are: systematic review of estimands. BMJ, 2022, 378: e070146.

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