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Trials

, 14:P139 | Cite as

Central statistical monitoring in multicentre clinical trials: developing statistical approaches for analysing key risk indicators

  • Elsa Valdes-Marquez
  • Jemma C Hopewell
  • Jane Armitage
  • Martin Landray
Open Access
Poster presentation
  • 629 Downloads

Keywords

Public Health Clinical Trial Patient Safety Statistical Approach Statistical Monitoring 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Objective

Central statistical monitoring (CSM) can identify trial misconduct, and help to prioritise on-site visits and additional training. Key risk indicators (KRIs) focus CSM on variables most likely to affect study reliability or patient safety. We developed the use of robust minimum covariance determinant (MCD) distances to detect outlying centres in the context of KRI analyses.

Method

Initially, a summary statistic (e.g. mean) describing the KRI is calculated for each centre and robust MCD-based distances are calculated by the FAST-MCD algorithm (Rousseeuw, 1999). For each KRI, robust estimates of the multivariate location Open image in new window and scatter Open image in new window are defined by minimizing the determinant of the covariance matrix and generalized distances, Open image in new window , are calculated. The distances follow a Open image in new window distribution (where n is the multivariate dimension) and define corresponding p-values. Outliers are defined as centres with p-values below a pre-defined threshold (e.g. p<0.05). This method can be used for univariate and multivariate KRIs. Furthermore, p-values can be combined across KRIs to form a single score. We provide empirical examples and graphical displays of univariate and multivariate KRI analyses undertaken using this robust MCD distance approach based on a large-scale multicentre cardiovascular trial.

Conclusion

Robust MCD distances offer a flexible approach for analysing both univariate and multivariate KRIs and can be implemented in standard statistical software.

Copyright information

© Valdes-Marquez et al; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Elsa Valdes-Marquez
    • 1
  • Jemma C Hopewell
    • 1
  • Jane Armitage
    • 1
  • Martin Landray
    • 1
  1. 1.Clinical Trial Service Unit and Epidemiological Studies UnitUniversity of OxfordOxfordUK

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