Identifying falsified clinical data

Joanne Lee and George Judge ()

No 47001, CUDARE Working Papers from University of California, Berkeley, Department of Agricultural and Resource Economics

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 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.

Keywords: Health Economics and Policy; Research and Development/Tech Change/Emerging Technologies (search for similar items in EconPapers)
Pages: 8 p
Date: 2008-12
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https://ageconsearch.umn.edu/record/47001/files/CUDARE%201073%20Judge.pdf (application/pdf)

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Working Paper: Identifying falsified clinical data (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:ucbecw:47001

DOI: 10.22004/ag.econ.47001

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