International Drug Development Institute, Louvain-la-Neuve, Belgium.
Clin Trials. 2012 Dec;9(6):705-13. doi: 10.1177/1740774512447898. Epub 2012 Jun 8.
Classical monitoring approaches rely on extensive on-site visits and source data verification. These activities are associated with high cost and a limited contribution to data quality. Central statistical monitoring is of particular interest to address these shortcomings.
This article outlines the principles of central statistical monitoring and the challenges of implementing it in actual trials.
A statistical approach to central monitoring is based on a large number of statistical tests performed on all variables collected in the database, in order to identify centers that differ from the others. The tests generate a high-dimensional matrix of p-values, which can be analyzed by statistical methods and bioinformatic tools to identify extreme centers.
Results from actual trials are provided to illustrate typical findings that can be expected from a central statistical monitoring approach, which detects abnormal patterns that were not (or could not have been) detected by on-site monitoring.
Central statistical monitoring can only address problems present in the data. Important aspects of trial conduct such as a lack of informed consent documentation, for instance, require other approaches. The results provided here are empirical examples from a limited number of studies.
Central statistical monitoring can both optimize on-site monitoring and improve data quality and as such provides a cost-effective way of meeting regulatory requirements for clinical trials.
传统监测方法依赖于大量现场访问和源数据验证。这些活动成本高昂，对数据质量的贡献有限。中心统计监测尤其值得关注，以解决这些缺点。
本文概述了中心统计监测的原则，以及在实际试验中实施它所面临的挑战。
中心监测的统计方法基于对数据库中收集的所有变量进行大量统计检验，以识别与其他中心不同的中心。这些检验会生成一个高维的 p 值矩阵，可以通过统计方法和生物信息学工具进行分析，以识别极端中心。
提供了实际试验的结果，以说明可以预期从中心统计监测方法中得到的典型发现，该方法检测到了通过现场监测无法（或不可能）检测到的异常模式。
中心统计监测只能解决数据中存在的问题。例如，试验进行中缺乏知情同意文件等重要方面需要采用其他方法。这里提供的结果是来自有限数量研究的经验实例。
中心统计监测可以优化现场监测，提高数据质量，因此是满足临床试验监管要求的一种具有成本效益的方法。
