title: [Clinical trial data management and quality metrics system].
abstract: Data quality management system is essential to ensure accurate, complete, consistent, and reliable data collection in clinical research. This paper is devoted to various choices of data quality metrics. They are categorized by study status, e.g. study start up, conduct, and close-out. In each category, metrics for different purposes are listed according to ALCOA+ principles such us completeness, accuracy, timeliness, traceability, etc. Some general quality metrics frequently used are also introduced. This paper contains detail information as much as possible to each metric by providing definition, purpose, evaluation, referenced benchmark, and recommended targets in favor of real practice. It is important that sponsors and data management service providers establish a robust integrated clinical trial data quality management system to ensure sustainable high quality of clinical trial deliverables. It will also support enterprise level of data evaluation and bench marking the quality of data across projects, sponsors, data management service providers by using objective metrics from the real clinical trials. We hope this will be a significant input to accelerate the improvement of clinical trial data quality in the industry.
authors.forename: Zhao-hua
authors.initials: ZH
authors.lastname: Chen
authors.forename: Qin
authors.initials: Q
authors.lastname: Huang
authors.forename: Ya-zhong
authors.initials: YZ
authors.lastname: Deng
authors.forename: Yue
authors.initials: Y
authors.lastname: Zhang
authors.forename: Yu
authors.initials: Y
authors.lastname: Xu
authors.forename: Hao
authors.initials: H
authors.lastname: Yu
authors.forename: Zong-fan
authors.initials: ZF
authors.lastname: Liu
mesh_terms: D019985:Benchmarking; D002986:Clinical Trials as Topic; D003625:Data Collection / Q000592:standards*; D016247:Information Storage and Retrieval / Q000592:standards*; D011786:Quality Control