美国食品和药物管理局 (FDA) 通过中央统计监测工具的经验
Xiaofeng Tina Wang1, Paul Schuette1, Matilde Kam1
1FDA/CDER/OTS/OB/DAI (Food and Drug Administration, Center for Drug Evaluation and Research, Office of Translational Sciences, Office of Biostatistics, Division of Analytics and Informatics), Silver Spring, Maryland, United States.
Journal of biopharmaceutical statistics
|March 29, 2024
概括
集中统计监测 (CSM) 通过根据数据不一致性对网站进行排名,有效地识别临床试验中的数据异常. 这种通过设计的质量方法提高了数据完整性,并支持监管审查.
科学领域:
- 临床试验方法论 临床试验方法论
- 统计数据分析 统计数据分析
- 监管科学是一种监管科学.
背景情况:
- 美国食品和药物管理局 (FDA) 通过指导文件支持临床试验的设计质量 (QbD).
- 集中统计监测 (CSM) 是QbD的一个组成部分,用于确保试验质量和数据完整性.
- 一项合作研究和开发协议 (CRADA) 促进了CluePoints和FDA之间的CSM平台的应用.
研究的目的:
- 在临床试验环境中描述CSM平台的经验和应用.
- 证明CSM在识别外围临床试验场所和数据异常方面的有效性.
- 为统计审查人员提供有关敏感性和子组分析数据驱动方法的见解.
主要方法:
- 利用CSM平台对主体级数据进行了大量的统计测试,以确定异常点.
- 计算了每个站点的整体数据不一致性得分,并根据聚合的p值对它们进行排名.
- 进行了敏感性分析,不包括实验室和问卷数据,并根据地点,国家/地区和患者进行了分析.
主要成果:
- 该CSM平台成功地在一项未被识别的试验中发现了典型的数据异常,说明了异常的数据模式.
- 对选定的终点进行了关键风险指标分析.
- 讨论了潜在的数据异常及其原因,强调了数据驱动方法的有效性.
结论:
- CSM是一种有效和高效的方法,用于识别有数据异常的临床试验场所.
- 这种数据驱动的方法为统计审查员提供了宝贵的见解,有助于进一步的分析和探索.
- 诸如数据混乱和外部干扰 (例如COVID-19) 等挑战可能会影响数据质量和监控工作.
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