Identifying falsified clinical data

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 Benford’s 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.


Issue Date:
2008-12
Publication Type:
Working or Discussion Paper
PURL Identifier:
http://purl.umn.edu/47001
Total Pages:
8 p
Series Statement:
CUDARE Working Papers
1073




 Record created 2017-04-01, last modified 2017-08-25

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