Strategic Shift of Statistical Review on Data Quality Assessment for New Drug Applications in China. (March 2019)

• Record Type: Journal Article
• Authors: Wang, Jun; Wang, Gang; Li, Min; Han, Jingjing; Zeng, Xin; Pan, Jianhong; Yang, Jinbo
• Is Part Of: Therapeutic innovation & regulatory science. Volume 53:Number 2(2019:Mar.)
• Page Start: 227
• Page End: 232
• Publication Date: 2019-03
• Subjects: data quality -- statistical review -- regulatory transformation -- risk-based monitoring -- new drug application -- CFDA; Drugs -- Periodicals; Pharmacology -- Periodicals; Drugs -- Research -- Periodicals; Drugs -- Testing -- Periodicals; 615.1
• DOI: 10.1177/2168479018778528
• Languages: English
• ISSNs: 2168-4790

Abstract

Data quality is critical for clinical trials to obtain robust conclusions about drug safety and efficacy evaluation. Effective data quality evaluation has been one of the major obstacles to new drug approvals in China, which hinders innovation in drug discovery and development ultimately. To improve the data quality submitted for regulatory drug approval, the China Food and Drug Administration (CFDA) has issued serial official announcements and industry guidelines regarding improvement of the clinical trial data integrity and quality since 2015. These announcements and follow-up measures are shaping up the entire pharmaceutical industry in China. While data quality is being strongly emphasized more than ever at the trial conduction phase, it is still an open question about how to assess data quality effectively at the review stage. Thus, this article describes the authors' standpoints to assess the quality and integrity of submitted clinical data via statistical review methods including advanced risk-based approaches, which may bring significant impact to new drug applications and motivate sustainable development of innovative medicines in China.