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 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.
Bibtex:
@techreport{,
title={Identifying falsified clinical data},
author={Lee, Joanne and Judge, George G},
journal={Department of Agricultural \& Resource Economics, UCB},
year={2008},
URL={https://escholarship.org/uc/item/8x00h1c1},
}
Reference Type: E-Print
Subject Area(s): Statistics