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

Clin Trials. 2012 Dec;9(6):705-13. doi: 10.1177/1740774512447898. Epub 2012 Jun 8.


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.

Purpose: This article outlines the principles of central statistical monitoring and the challenges of implementing it in actual trials.

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.

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.

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.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bias
  • Clinical Trials Data Monitoring Committees
  • Data Interpretation, Statistical*
  • Multicenter Studies as Topic / ethics
  • Multicenter Studies as Topic / methods
  • Multicenter Studies as Topic / standards*
  • Quality Control
  • Research Design
  • Scientific Misconduct