Key technical considerations for the evaluation of clinical value of drugs using real-world data_Chinese Journal of Evidence-Based Medicine

Authors：

XU Jiayue ^1,2 , HE Qiao ^1,2 , LIU Yanmei ^1,2 , LIU Jiali ^1,2 , YAO Minghong ^1,2 , ZHAO Yang ³ , LI Guowei ⁴ , HUANG Lihong ⁵ , SHU Xiaochen ⁶ ,  LI Ling ^1,2 ,  WANG Wen ^1,2 ,  SUN Xin ^1,2

1. Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, P. R. China;
3. School of Public Health Nanjing Medical University, Nanjing 211166, P. R. China;
4. Guangdong Second Provincial General Hospital, Jinan University, Guangzhou 510317, P. R. China;
5. Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China;
6. Department of Epidemiology and Biostatistics, School of Public Health, Medical College Soochow University, Suzhou 215123, P. R. China;

Corresponding author：

LI Ling, Email: liling@wchscu.cn; WANG Wen, Email: wangwen83@outlook.com; SUN Xin, Email: sunxin@wchscu.cn

Keywords：

Real-world data; Clinical value evaluation; Health insurance decision; Technical guidance

DOI：

10.7507/1672-2531.202401084

Video：

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Assessing the clinical value of pharmaceuticals is crucial for comprehensive evaluation in clinical practice and plays a vital role in supporting decision-making for drug supply assurance. Real-world data (RWD) offers valuable insights into the actual diagnosis and treatment processes, serving as a significant data source for evaluating the clinical demand, effectiveness, and safety of drugs. This technical guidance aims to elucidate the scope of application of RWD for the clinical value assessment of pharmaceuticals, as well as the key considerations for conducting value assessment research. These considerations include identifying the dimensions of clinical value that necessitate RWD and effectively utilizing RWD for evaluation purposes. Additionally, this guidance provides essential points for implementing pharmaceutical clinical value assessment based on real-world data, with a specific focus on study design and statistical analysis. By doing so, this guidance assists researchers in accurately comprehending and standardizing the utilization of real-world research in conducting pharmaceutical clinical research.

Citation： XU Jiayue, HE Qiao, LIU Yanmei, LIU Jiali, YAO Minghong, ZHAO Yang, LI Guowei, HUANG Lihong, SHU Xiaochen, LI Ling, WANG Wen, SUN Xin. Key technical considerations for the evaluation of clinical value of drugs using real-world data. Chinese Journal of Evidence-Based Medicine, 2024, 24(5): 523-528. doi: 10.7507/1672-2531.202401084 Copy

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2.	国家卫生健康委. 药品临床综合评价管理指南(2021年版试行)(国卫办药政发〔2021〕16号). 2021.
3.	海南省医疗保障局. 海南省医疗保障局关于推进真实世界数据医保创新应用的若干意见(琼医保〔2023〕24号). 2023.
4.	Wang W, Tan J, Wu J, et al. Use of real world data to improve drug coverage decisions in China. BMJ, 2023, 381: e068911.
5.	刘炳林, 薛斐然. 药物临床价值评估的主要考虑因素及问题. 中国新药杂志, 2017, 26(5): 504-508.
6.	孙鑫, 谭婧, 唐立, 等. 基于真实世界证据的上市后药品评价技术框架体系: 思考与建议. 中国循证医学杂志, 2018, 18(4): 277-283.
7.	Agrawal S, Arora S, Amiri-Kordestani L, et al. Use of single-arm trials for US food and drug administration drug approval in oncology, 2002-2021. JAMA Oncol, 2023, 9(2): 266-272.
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9.	孙鑫, 谭婧, 唐立, 等. 重新认识真实世界研究. 中国循证医学杂志, 2017, 17(2): 126-130.
10.	谭婧, 彭晓霞, 舒啸尘, 等. 患者登记数据库构建技术规范. 中国循证医学杂志, 2019, 19(7): 771-778.
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13.	Cox E, Martin BC, Van Staa T, et al. Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society For Pharmacoeconomics and Outcomes Research Good Research Practices For Retrospective Database Analysis Task Force report--Part II. Value Health, 2009, 12(8): 1053-1061.
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15.	温泽淮, 李玲, 刘艳梅, 等. 实效性随机对照试验的技术规范. 中国循证医学杂志, 2019, 19(7): 794-802.
16.	Loudon K, Treweek S, Sullivan F, et al. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ, 2015, 350: h2147.
17.	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.
18.	FDA. Best practices for conducting and reporting pharmacoepidemiologic safety studies using electronic healthcare data. 2013.
19.	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.
20.	Yoshida K, Solomon DH, Kim SC. Active-comparator design and new-user design in observational studies. Nat Rev Rheumatol, 2015, 11(7): 437-441.
21.	Kyriacou DN, Lewis RJ. Confounding by indication in clinical research. JAMA, 2016, 316(17): 1818-1819.
22.	Yadav K, Lewis RJ. Immortal time bias in observational studies. JAMA, 2021, 325(7): 686-687.
23.	Seeger JD, Davis KJ, Iannacone MR, et al. Methods for external control groups for single arm trials or long-term uncontrolled extensions to randomized clinical trials. Pharmacoepidemiol Drug Saf, 2020, 29(11): 1382-1392.
24.	Schmidli H, Häring DA, Thomas M, et al. Beyond randomized clinical trials: use of external controls. Clin Pharmacol Ther, 2020, 107(4): 806-816.
25.	Carpenter JR, Smuk M. Missing data: a statistical framework for practice. Biom J, 2021, 63(5): 915-947.
26.	严洁. 缺失数据的多重插补. 重庆: 重庆大学出版社, 2017.

1. Wieseler B, Neyt M, Kaiser T, et al. Replacing RCTs with real world data for regulatory decision making: a self-fulfilling prophecy. BMJ, 2023, 380: e073100.
2. 国家卫生健康委. 药品临床综合评价管理指南(2021年版试行)(国卫办药政发〔2021〕16号). 2021.
3. 海南省医疗保障局. 海南省医疗保障局关于推进真实世界数据医保创新应用的若干意见(琼医保〔2023〕24号). 2023.
4. Wang W, Tan J, Wu J, et al. Use of real world data to improve drug coverage decisions in China. BMJ, 2023, 381: e068911.
5. 刘炳林, 薛斐然. 药物临床价值评估的主要考虑因素及问题. 中国新药杂志, 2017, 26(5): 504-508.
6. 孙鑫, 谭婧, 唐立, 等. 基于真实世界证据的上市后药品评价技术框架体系: 思考与建议. 中国循证医学杂志, 2018, 18(4): 277-283.
7. Agrawal S, Arora S, Amiri-Kordestani L, et al. Use of single-arm trials for US food and drug administration drug approval in oncology, 2002-2021. JAMA Oncol, 2023, 9(2): 266-272.
8. 王雯, 谭婧, 任燕, 等. 重新认识真实世界数据研究: 更新与展望. 中国循证医学杂志, 2020, 20(11): 1241-1246.
9. 孙鑫, 谭婧, 唐立, 等. 重新认识真实世界研究. 中国循证医学杂志, 2017, 17(2): 126-130.
10. 谭婧, 彭晓霞, 舒啸尘, 等. 患者登记数据库构建技术规范. 中国循证医学杂志, 2019, 19(7): 771-778.
11. Ford I, Norrie J. Pragmatic trials. N Engl J Med, 2016, 375(5): 454-463.
12. 王雨宁, 姚明宏, 任燕, 等. 真实世界数据作为外部对照用于药械临床评价的特殊考虑. 中华流行病学杂志, 2023, 44(2): 335-340.
13. Cox E, Martin BC, Van Staa T, et al. Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society For Pharmacoeconomics and Outcomes Research Good Research Practices For Retrospective Database Analysis Task Force report--Part II. Value Health, 2009, 12(8): 1053-1061.
14. 国家药品监督管理局药品审评中心. 药物真实世界研究设计与方案框架指导原则(试行). 2023.
15. 温泽淮, 李玲, 刘艳梅, 等. 实效性随机对照试验的技术规范. 中国循证医学杂志, 2019, 19(7): 794-802.
16. Loudon K, Treweek S, Sullivan F, et al. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ, 2015, 350: h2147.
17. 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.
18. FDA. Best practices for conducting and reporting pharmacoepidemiologic safety studies using electronic healthcare data. 2013.
19. 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.
20. Yoshida K, Solomon DH, Kim SC. Active-comparator design and new-user design in observational studies. Nat Rev Rheumatol, 2015, 11(7): 437-441.
21. Kyriacou DN, Lewis RJ. Confounding by indication in clinical research. JAMA, 2016, 316(17): 1818-1819.
22. Yadav K, Lewis RJ. Immortal time bias in observational studies. JAMA, 2021, 325(7): 686-687.
23. Seeger JD, Davis KJ, Iannacone MR, et al. Methods for external control groups for single arm trials or long-term uncontrolled extensions to randomized clinical trials. Pharmacoepidemiol Drug Saf, 2020, 29(11): 1382-1392.
24. Schmidli H, Häring DA, Thomas M, et al. Beyond randomized clinical trials: use of external controls. Clin Pharmacol Ther, 2020, 107(4): 806-816.
25. Carpenter JR, Smuk M. Missing data: a statistical framework for practice. Biom J, 2021, 63(5): 915-947.
26. 严洁. 缺失数据的多重插补. 重庆: 重庆大学出版社, 2017.

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