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

Abstract

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.