MacDougall, M (2014). Assessing the Integrity of Clinical Data: When is Statistical Evidence Too Good to be True?. Topoi 33(2), pp. 323–337.
This work cites the following items of the Benford Online Bibliography:
Al-Marzouki, S, Evans, S, Marshall, T and Roberts, I (2005). Are these data real? Statistical methods for the detection
of data fabrication in clinical trials. Brit. Med. J 331, pp. 267-270. DOI:10.1136/bmj.331.7511.267.
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Buyse, M, George, SL, Evans, S, Geller, NL, Edler, L and Hutton, J (1999). The Role of Biostatistics in the Prevention, Detection and Treatment of Fraud in Clinical Trials. Statistics in Medicine 18 (24), pp. 3435-3451. ISSN/ISBN:0277-6715. DOI:10.1002/(SICI)1097-0258(19991230)18:24<3435::AID-SIM365>3.0.CO;2-O.
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de Vocht, F and Kromhout, H (2013). The use of Benford's law for evaluation of quality of occupational hygiene data. Ann Occup Hyg. 57(3), pp. 296-304. DOI:10.1093/annhyg/mes067.
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Nigrini, MJ (1999). I’ve got your number. Journal of Accountancy 187(5), pp. 79-83.
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Singleton, TW (2011). Understanding and applying Benford’s Law. ISACA Journal, v.3 pp.1-4.
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Smith, CA (2002). Detecting Anomalies in Your Data Using Benford’s Law. Paper 249 (Statistics and Data Analysis) in: Proceedings of SUGI 27, Orlando, USA, April 14-17.
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Taylor, RN, McEntegart, DJ and Stillman, EC (2002). Statistical techniques to detect fraud and other data irregularities in clinical questionnaire data. Drug Information Journal 36, 115-125. DOI:10.1177/009286150203600115.
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