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Lee, J and Judge, GC (2008)

Identifying falsified clinical data

CUDARE working paper 1073, University of California, Berkeley.

ISSN/ISBN: Not available at this time. DOI: Not available at this time.

Abstract: Clinical data serve as a necessary basis for medical decisions. Consequently, the importance of methods that help officials quickly identify human tampering of data cannot be underestimated. In this paper, we suggest Benfordís Law as a basis for objectively identifying the presence of experimenter distortions in the outcome of clinical research data. We test this tool on a clinical data set that contains falsified data and discuss the implications of using this and information-theoretic methods as a basis for identifying data manipulation and fraud.

@techreport{, title={Identifying falsified clinical data}, author={Lee, Joanne and Judge, George G}, journal={Department of Agricultural \& Resource Economics, UCB}, year={2008}, URL={}, }

Reference Type: E-Print

Subject Area(s): Statistics