The detection of fraud and other systematic data irregularities in clinical trials is an important issue. While awareness of the problem is growing and willingness to combat it is clear, there still appears to be a lack of detection procedures suitable for routine implementation by trial coordinators. The shortage is particularly acute for discrete data, since the majority of methods which are available have been developed for continuous responses. In this paper, we examine the suitability of existing methods for discrete outcomes and propose a new technique for questionnaire data in both an informal graphical mode and as a randomization test. This method exploits the underlying correlation structure of a questionnaire and the difficulty in fabricating such details. A data set concerning a trial of a novel drug for treatment of schizophrenia, in which the Brief Psychiatric Rating Scale was used to assess patient mental health, is used for illustration.
This is a preview of subscription content, log in to check access.
Buy single article
Instant unlimited access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Buyse M, George SL, Evans S, Geiler NL, Ranstam J, Scherrer B, Lesaffre E, Murray G, Edler L, Hutton J, Colton T, Lachenbruch P, Verma BL. The role of biostatistics in the prevention, detection and treatment of fraud in clinical trials. Report of the International Society of Clinical Biostatistics Subcommittee on Fraud. Stat Med. 1999;18(24):3435–3451.
Schmidt J, Gertzen H, Aschenbrenner KM, Ryholt-Jensen S. Detecting fraud using auditing and biomet-rical methods. Appl Clin Trials. 1995;4(5):40–49.
O’Donnell P. Facing up to fraud. Appl Clin Trials. 1993;2(3):36–40.
Lock S. Research misconduct: a resume of recent events. In: Lock S, Wells F, eds. Fraud and Misconduct in Medical Research. 2nd edition. London: BMJ Publishing Group; 1996:14–39.
Wells F. Investigating fraud—again. Appl Clin Trials. 2000;10(2):26–27.
Ranstam J, Buyse M, George SL, Evans S, Geiler NL, Scherrer B, Lesaffre E, Murray G, Edler L, Hutton JL, Colton T, Lachenbruch P. Fraud in medical research: An international survey of biostatisticians. Control Clin Trials. 2000;21:415–427.
Weiss RB, Vogelzang NJ, Peterson BA, Panasci LC, Carpenter JC, Gavigan M, Sartell K, Frei E, McIntyre OR. A successful system of scientific data audits for clinical trials. JAMA. 1995:270;459–464.
Hone J. Combating fraud and misconduct in medical research. Scrip Magazine. 1993:March;14–15.
Collins M, Evans S, Moynihan J, Piper D, Thomas P, Wells F. Statistical Techniques for the Investigation of Fraud in Clinical Research. London, England: Association of the British Pharmaceutical Industry Fraud Statistics Working Party; 1993.
Evans SJW. Detection of Fraud. In: Armitage P, Colton T, eds. Encyclopedia of Biostatistics. Chichester: John Wiley & Sons; 1998: 1583–1588.
Evans SJW. Statistical aspects of the detection of fraud. In: Lock S, Wells F, eds. Fraud and Misconduct in Medical Research. 2nd edition. London: BMJ Publishing Group; 1996:226–239.
Mosimann JE, Wiseman CV, Edelman RE. Data fabrication: Can people generate random digits? Account Research. 1995;4:31–55.
Overall JE, Gorham DR. The Brief Psychiatric Rating Scale. Psychol Rep. 1962;10:799–812.
Chernoff H. The use of faces to represent points in k-dimensional space graphically. J Am Stat Assoc. 1973;68(342):361–368.
S-PLUS. Seattle, WA: Statistical Sciences, Inc; 2000.
Preece DA. Distribution of final digits in data. The Statistician. 1981;30(1):31–60.
Newman TG, Odell PL. The Generation of Random Variates. No. 29 of Griffin’s Statistical Monographs & Courses. Stuart, A, ed. London; Griffin; 1971.
Horowitz AM. Fraud and scientific misconduct in the United States. In: Lock S, Wells F, eds. Fraud and Misconduct in Medical Research. 2nd edition. London: BMJ Publishing Group; 1996:144–165.
Manly BFJ. Randomization and Monte Carlo Methods in Biology. London: Chapman & Hall; 1991.
Bailey K. Detecting fabrication of data in a multicentre collaborative animal study. Control Clin Trials. 1991;12:741–752.
Larntz K, Perlman MD. A Simple Test for the Equality of Correlation Matrices. In: Gupta SS, Berger JO, eds. Statistical Decision Theory and Related Topics IV, Vol 2. New York, NY: Springer-Verlag; 1988:289–298.
Koziol JA, Alexander JE, Bauer LO, Kuperman S, Morzorati S, O’Connor SJ, Rohrbaugh J, Porjesze B, Begleiter H, Polich J. A graphical technique for displaying correlation matrices. Am Statistician. 1997;51:301–304.
Walsh RC. Systematic measures for the prevention and early detection of investigator fraud. Drug Inf J. 1994;28:1161–1165.
Mackintosh DR, Zepp VJ. Detection of negligence, fraud and other bad faith efforts during field auditing of clinical trial sites. Drug Inf J. 1996;30:645–653.
About this article
Cite this article
Taylor, R.N., McEntegart, D.J. & Stillman, E.C. Statistical Techniques to Detect Fraud and Other Data Irregularities in Clinical Questionnaire Data. Ther Innov Regul Sci 36, 115–125 (2002) doi:10.1177/009286150203600115
- Clinical trials
- Clinical questionnaires
- Correlation structure
- Brief Psychiatric Rating Scale