Detecting data fabrication in clinical trials from cluster analysis perspective
Article first published online: 8 OCT 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Volume 10, Issue 3, pages 257–264, May/June 2011
How to Cite
Wu, X. and Carlsson, M. (2011), Detecting data fabrication in clinical trials from cluster analysis perspective. Pharmaceut. Statist., 10: 257–264. doi: 10.1002/pst.462
- Issue published online: 16 MAY 2011
- Article first published online: 8 OCT 2010
- clinical trials;
- cluster analysis;
- data fabrication
Detecting data fabrication is of great importance in clinical trials. As the role of statisticians in detecting abnormal data patterns has grown, a large number of statistical procedures have been developed, most of which are based on descriptive statistics. Based upon the fact that substantial data fabrication cases have certain clustering structures, this paper discusses the potential for the use of statistical clustering method in fraud detection. Three clustering patterns, angular, neighborhood and repeated measurements clustering, are identified and explored. Correspondingly, simple and efficient test statistics are proposed and randomization tests are carried out. The proposed methods are applied to a 12-week multi-center study for illustration. Extensive simulations are conducted to validate the effectiveness of the procedures. Copyright © 2010 John Wiley & Sons, Ltd.