A statistical approach to central monitoring of data quality in clinical trials

作者：Venet David; Doffagne Erik; Burzykowski Tomasz; Beckers Francois; Tellier Yves; Genevois Marlin Eric; Becker Ursula; Bee Valerie; Wilson Veronique; Legrand Catherine; Buyse Marc^*

来源：Clinical Trials, 2012, 9(6): 705-713.

DOI：10.1177/1740774512447898

Background 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. %26lt;br%26gt;Purpose This article outlines the principles of central statistical monitoring and the challenges of implementing it in actual trials. %26lt;br%26gt;Methods 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. %26lt;br%26gt;Results 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. %26lt;br%26gt;Limitations 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. %26lt;br%26gt;Conclusion 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. Clinical Trials 2012; 9: 705-713. http://ctj.sagepub.com

• 出版日期2012-12