摘要: 临床试验数据质量管理体系是确保临床研究数据真实完整、一致可靠的关键。本文中所收录的临床试验数据管理评价指标, 按照临床试验的进展过程归纳为在启动阶段、进行阶段及结束阶段分别适用的指标。在每一个进展阶段中, 又依据数据质量ALCOA+原则列举了不同目的的评价指标, 如体现数据完整性、准确性、及时性、可溯源性等方面的指标。最后列举了一些综合质量管理常用的相关指标。本文对每一个指标的定义、目的、评价结果、建议标准及达标要求等进行了逐一描述。力求详实具体, 并具有实际可行的指导意义。建立完整可行的综合指标体系、全面管理临床试验数据质量, 不仅可以帮助申办者或数据管理机构达到全面、系统、可持续管理单个临床试验数据质量的目的, 同时也可以为综合评价多个项目之间、不同申办机构之间、以及不同数据管理机构之间的临床试验数据质量对比奠定基础, 提供重要而且客观的实际依据, 使建立行业通用的质量标准成为可能, 并助力推动整个行业共同快速进步。

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.