Establishing the irrefutable statistical standards which ORI and Article III and state courts must apply in determining data validity. A call for impeachment, debarment and Presidential Pardon

Main Article Content

Richard M Fleming
Tapan K Chaudhuri
Gordon M Harrington

Abstract



A considerable amount of attention has recently been focused on addressing issues related to data fraud. As this specific example shows, statistical analysis can be used to determine when data fabrication, falsification or plagiarism has occurred. Presented here is an example of statistical data analysis showing how the original data (HI data) set, reported as being fabricated, was in fact statistically shown to be valid/real data; while another set of data (Hansen data) was reported as fabricated and was statistically shown to be falsified and plagiarized from the original HI data. This particular case looked at changes in weight related to a diet study – with implications for its impact on heart disease and cancer–as evidenced by the involvement of Revival Soy and Avon.


This paper should be used not only for scientific publication analysis of data fraud with particular emphasis in Cardiology and Oncology, but it should also set the irrefutable standard for data validity and fraud analysis for ORI and foe all U.S. and International Courts.



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Fleming, R. M., Chaudhuri, T. K., & Harrington, G. M. (2020). Establishing the irrefutable statistical standards which ORI and Article III and state courts must apply in determining data validity. A call for impeachment, debarment and Presidential Pardon. Journal of Cardiovascular Medicine and Cardiology, 7(1), 006–023. https://doi.org/10.17352/2455-2976.000105
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Copyright (c) 2020 Fleming RM, et al.

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Akhtar-Danesh N, Dehghan-Kooshkghazi M (2003) How does correlation structure differ between real and fabricated data-sets? BMC Med Res Methodol 3: 18-26. Link: http://bit.ly/37FGgJy

Buyse M, George SL, Evans S, Geller NL, Ranstam J, et al. (1999) The role of biostatistics in the prevention, detection and treatment of fraud in clinical trials. Statistics in Medicine 18: 3435-3451. Link: http://bit.ly/2RAWtdD

Hill TP (1998) The first digit phenomenon. American Scientist 86: 358-363. Link: https://b.gatech.edu/37CSliQ

Hill TP (1996) A statistical derivation of the significant-digit law. Statistics in Science 10: 354-363. Link: http://bit.ly/36Ab4tW

Al-Marzouki S, Evans S, Marshall T, Roberts I (2005) Are these data real? Statistical methods for the detection of data fabrication in clinical trials. British Medical Journal 331: 267-270. Link: http://bit.ly/2UgPqbV

Mosimann JE, Ratnaparkhi MV (1996) Uniform occurrence of digits for folded and mixture distributions on finite intervals. Communications in Statistics – Simulation and Computation 25: 481-506. Link: http://bit.ly/3b1m6Mq

O’Kelly M (2004) Using statistical techniques to detect fraud: a test case. Pharmaceutical Statistics 3: 237-246. Link: http://bit.ly/3aWIiHB

Walter CF, Richards III EP (2001) Using data digits to identify fabricated data. IEEE Engineering in Medicine and Biology 20: 96-100. Link: http://bit.ly/38Lttpa

Best M, Neuhauser D (2006) Walter A Shewhart, 1924, and the Hawthorne factory. Qual Saf Health Care 15: 142-143. Link: http://bit.ly/36X2UvY