Developing technical guidance for real-world data and studies to achieve better production and use of real-world evidence in China_Chinese Journal of Evidence-Based Medicine

Authors：

 SUN Xin ¹ , TAN Jing ¹ , WANG Wen ¹ , GAO Pei ² , PENG Xiaoxia ³ , WEN Zehuai ⁴ , WANG Li ⁵ , WU Jing ⁶ , SHU Xiaochen ⁷ , WANG Yang ⁸ , LUO Jianfeng ⁹ , LI Ling ¹ , LI Youping ¹ , YAO Chen ¹⁰ , ZHAO Kun ¹¹ , CHEN Yingyao ¹² , ZHAI Suodi ¹³ , ZHAN Siyan ² , WU Jiuhong ¹⁴ , GUO Jianfei ¹⁵ , LV Zhiqiang ¹⁶ , XIE Feng ¹⁷ , Gordon Guyatt ¹⁷ , On behalf of China REal world data and studies ALliance (ChinaREAL)

1. Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, P.R.China;
2. Department of Epidemiology and Biostatistics, Peking University Health Science Center, Beijing, 100191, P.R.China;
3. Center for Clinical Epidemiology and Evidence-based Medicine, Beijing Children’s Hospital, Capital Medical University, National Center for Children Health, Beijing, 100045, P.R.China;
4. Key Unit of Methodology in Clinical Research, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, P.R.China;
5. Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, P.R.China;
6. School of Pharmacology Science and Technology, Tianjin University, Tianjin, 300072, P.R.China;
7. Department of Epidemiology and Statistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, P.R.China;
8. Medical Research & Biometrics Center, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100037, P.R.China;
9. Department of Biostatistics, School of Public Health, Fudan University, Shanghai, 200032, P.R.China;
10. Peking University First Hospital, Beijing, 100034, P.R.China;
11. China National Health Development Research Center, Beijing, 100044, P.R.China;
12. School of Public Health, Fudan University, Shanghai, 200032, P.R.China;
13. Department of Pharmacy, Peking University Third Hospital, Beijing, 100191, P.R.China;
14. Department of Pharmacy, Beijing 306 Hospital, Beijing, 100083, P.R.China;
15. College of Pharmacy, University of Cincinnati, Ohio, 45221, USA;
16. Department of Clinical Pharmacy and Outcome Research, University of South Carolina, Columbia, 29208, USA;
17. Department of Clinical Epidemiology and Biostatistics, University of Macmaster, Hamilton, L8S4L8, Canada;

Corresponding author：

SUN Xin, Email: sunx79@hotmail.com

Keywords：

Real-world data; Real-world study; Real-world evidence; Technical guidance

DOI：

10.7507/1672-2531.201905082

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Abstract Full text Figures/Tables Video References Cited by

With the boom of information technology and data science, real-world evidence (RWE) which is produced using diverse real-world data (RWD) has become an important source for healthcare practice and policy decisions, such as regulatory and coverage decisions, guideline development, and disease management. The production of high-quality RWE requires not only complete, accurate and usable data, but also scientific and sound study designs and data analyses to enable the questions of interest to be reliably answered. In order to improve the quality of production and use of RWE, China REal world data and studies ALliance (ChinaREAL) has developed the first series of technical guidance for developing real-world data and subsequent studies. The efforts are ongoing which would ultimately inform better healthcare practice and policy decisions.

Citation： SUN Xin, TAN Jing, WANG Wen, GAO Pei, PENG Xiaoxia, WEN Zehuai, WANG Li, WU Jing, SHU Xiaochen, WANG Yang, LUO Jianfeng, LI Ling, LI Youping, YAO Chen, ZHAO Kun, CHEN Yingyao, ZHAI Suodi, ZHAN Siyan, WU Jiuhong, GUO Jianfei, LV Zhiqiang, XIE Feng, Gordon Guyatt, On behalf of China REal world data and studies ALliance (ChinaREAL). Developing technical guidance for real-world data and studies to achieve better production and use of real-world evidence in China. Chinese Journal of Evidence-Based Medicine, 2019, 19(7): 755-762. doi: 10.7507/1672-2531.201905082 Copy

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14.	Zhu L, Zheng WJ. Informatics, data science, and artificial intelligence. JAMA, 2018, 320(11): 1103-1104.
15.	Sun X, Tan J, Tang L, et al. Real world evidence: experience and lessons from China. BMJ, 2018, 360: j5262.
16.	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): 8-12.
17.	王雯, 刘艳梅, 谭婧, 等. 回顾性数据库研究的概念、策划与研究数据库构建. 中国循证医学杂志, 2018, 18(2): 230-237.
18.	谭婧, 程亮亮, 王雯, 等. 患者登记研究的策划与患者登记数据库构建: 基于观察性设计的真实世界研究. 中国循证医学杂志, 2017, 17(12): 1365-1372.
19.	唐立, 康德英, 喻佳洁, 等. 实效性随机对照试验: 真实世界研究的重要设计. 中国循证医学杂志, 2017, 17(9): 999-1004.
20.	李洪, 魏来, 郭晓蕙, 等. 真实世界研究伦理审查初探. 中国循证医学杂志, 2018, 18(11): 1198-1202.

1. U.S. Food & Drug Administration. Framework For FDA's Real World Evidence Progarm. Available at: https://www.fda.gov/media/120060/download.
2. U.S. Food & Drug Administration. Use of real-world evidence to support regulatory decision-making for medical devices. Available at: https://www.fda.gov/media/99447/download.
3. U.S. Food & Drug Administration. Best practice for conducting and reporting pharmacoepidemiologic safety studies using electronic healthcare data. Available at: https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM243537.pdf.
4. Behrman RE, Benner JS, Brown JS, et al. Developing the sentinel system-a national resource for evidence development. N Engl J Med, 2011, 364(6): 498-499.
5. Yount KW, Turrentine FE, Lau CL, et al. Putting the value framework to work in surgery. J Am Coll Surg, 2015, 220(4): 596-604.
6. Makady A, Ham RT, de Boer A, et al. Policies for use of real-world data in health technology assessment (HTA): a comparative study of six HTA agencies. Value Health, 2017, 20(4): 520-532.
7. Malone DC, Brown M, Hurwitz JT, et al. Real-world evidence: useful in the real world of US payer decision making? How? When? And What Studies? Value Health, 2018, 21(3): 326-333.
8. Garrison LP Jr, Neumann PJ, Erickson P, et al. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. Value Health, 2007, 10(5): 326-335.
9. Oyinlola JO, Campbell J, Kousoulis AA. Is real world evidence influencing practice? A systematic review of CPRD research in NICE guidances. BMC Health Serv Res, 2016, 16: 299.
10. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA, 2018, 319(13): 1317-1318.
11. Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA, 2018, 320(21): 2199-2200.
12. Stead WW. Clinical implications and challenges of artificial intelligence and deep learning. JAMA, 2018, 320(11): 1107-1108.
13. Hinton G. Deep learning-a technology with the potential to transform health care. JAMA, 2018, 320(11): 1101-1102.
14. Zhu L, Zheng WJ. Informatics, data science, and artificial intelligence. JAMA, 2018, 320(11): 1103-1104.
15. Sun X, Tan J, Tang L, et al. Real world evidence: experience and lessons from China. BMJ, 2018, 360: j5262.
16. 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): 8-12.
17. 王雯, 刘艳梅, 谭婧, 等. 回顾性数据库研究的概念、策划与研究数据库构建. 中国循证医学杂志, 2018, 18(2): 230-237.
18. 谭婧, 程亮亮, 王雯, 等. 患者登记研究的策划与患者登记数据库构建: 基于观察性设计的真实世界研究. 中国循证医学杂志, 2017, 17(12): 1365-1372.
19. 唐立, 康德英, 喻佳洁, 等. 实效性随机对照试验: 真实世界研究的重要设计. 中国循证医学杂志, 2017, 17(9): 999-1004.
20. 李洪, 魏来, 郭晓蕙, 等. 真实世界研究伦理审查初探. 中国循证医学杂志, 2018, 18(11): 1198-1202.

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