Guide on Methodological Standards in Pharmacoepidemiology in China (2nd edition) and their series interpretation (19): tool systems and methodological paradigms for common data model-based data analysis

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Author: WU Yunxiao ^1, ² NIE Xiaolu ³ ZHENG Yongqi ^1, ² WANG Conghui ⁴ WU Jiarui ⁵ SUN Yexiang ⁶ SONG Haibo ⁷  ZHAN Siyan ^1, ^2, ^8, ⁹  SUN Feng ^1, ^2, ^8, ^10, ^11, ¹²

Affiliation: 1.Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China 2.Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China 3.Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China 4.Inner Mongolia Center for Drug Evaluation and Pharmacovigilance, Hohhot 010010, China 5.School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China 6.Yinzhou District Center for Disease Control and Prevention, Ningbo 315101, Zhejiang province, China 7.Drug Evaluation Center, National Medical Products Administration, NMPA/NMPA Center for Innovation and Research in Regulatory Science, Beijing 100163, China 8.Center of Postmarketing Drug Safety Evaluation, Peking University Health Science Center, Beijing 100191, China 9.Clinical Epidemiology Research Center, Peking University Third Hospital, Beijing 100191, China 10.Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China 11.College of Traditional Chinese Medicine, Xinjiang Medical University, Urumqi 830017, China 12.School of Public Health, Shihezi University, Shihezi 832000, Xinjiang Uygur Autonomous Region, China

Keywords: Pharmacoepidemiology Methodology Guideline Common data model Real world evidence

DOI: 10.12173/j.issn.1005-0698.202606128

Reference: Wu YX, Nie XL, Zheng YQ, et al. Guide on Methodological Standards in Pharmacoepidemiology in China (2nd edition) and their series interpretation (19): tool systems and methodological paradigms for common data model-based data analysis[J]. Chinese Journal of Pharmacoepidemiology, 2026, 35(7): 721-731. DOI: 10.12173/j.issn.1005-0698.202606128.[Article in Chinese]  Copied

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Abstract

With the expanding use of real-world data in post-marketing drug safety evaluation, comparative effectiveness research, drug utilization studies, and regulatory decision-making, differences in data structures, terminology mappings, data quality, and analytic workflows across heterogeneous data sources have become major challenges affecting the reproducibility of research and the credibility of evidence. The common data model (CDM) provides foundational support for real-world studies across institutions, regions, and countries by harmonizing data structures, standard terminology, and analytic interfaces. In recent years, tool ecosystems represented by OHDSI/HADES and the FDA Sentinel Initiative have promoted CDM-based data analysis from traditional single databases statistical modeling toward a standardized, reproducible, diagnosable, and distributable model of networked evidence generation. Based on the requirements for real-world data quality, study design, bias control, and statistical analysis plans outlined in the Guidelines for Pharmacoepidemiologic Research

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References

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