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.	徐文涛, 于天琦, 游月媛, 等. 因果推断: 不同目标效应量及其可比性问题. 中国循证医学杂志, 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, 刘宝. 真实世界研究中的因果推断及粗化精准匹配. 中国药物经济学, 2019, 14(10): 38-42,46.
4.	卢存存, 张强, 雷超, 等. 使用队列和常规收集数据开展随机对照试验的报告清单(CONSORT-ROUTINE2021)解读. 中国循证医学杂志, 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, 赵厚宇, 周庆欣, 等. 真实世界证据与随机对照试验: RCTDUPLICATE项目方法学介绍. 药物流行病学杂志, 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.	任国强, 王于丹, 周云波. 科学研究中因果推断的方法、应用与展望—以个体健康研究为例. 人口与经济, 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.
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. 徐文涛, 于天琦, 游月媛, 等. 因果推断: 不同目标效应量及其可比性问题. 中国循证医学杂志, 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, 刘宝. 真实世界研究中的因果推断及粗化精准匹配. 中国药物经济学, 2019, 14(10): 38-42,46.
4. 卢存存, 张强, 雷超, 等. 使用队列和常规收集数据开展随机对照试验的报告清单(CONSORT-ROUTINE2021)解读. 中国循证医学杂志, 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, 赵厚宇, 周庆欣, 等. 真实世界证据与随机对照试验: RCTDUPLICATE项目方法学介绍. 药物流行病学杂志, 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. 任国强, 王于丹, 周云波. 科学研究中因果推断的方法、应用与展望—以个体健康研究为例. 人口与经济, 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.
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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