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Statistical Techniques to Detect Fraud and Other Data Irregularities in Clinical Questionnaire Data

  • Rosemary N. Taylor
  • Damian J. McEntegartEmail author
  • Eleanor C. Stillman
Article

Abstract

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.

Key Words

Fraud Clinical trials Clinical questionnaires Correlation structure Brief Psychiatric Rating Scale 

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References

  1. 1.
    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.CrossRefGoogle Scholar
  2. 2.
    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.Google Scholar
  3. 3.
    O’Donnell P. Facing up to fraud. Appl Clin Trials. 1993;2(3):36–40.Google Scholar
  4. 4.
    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.Google Scholar
  5. 5.
    Wells F. Investigating fraud—again. Appl Clin Trials. 2000;10(2):26–27.Google Scholar
  6. 6.
    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.CrossRefGoogle Scholar
  7. 7.
    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.CrossRefGoogle Scholar
  8. 8.
    Hone J. Combating fraud and misconduct in medical research. Scrip Magazine. 1993:March;14–15.Google Scholar
  9. 9.
    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.Google Scholar
  10. 10.
    Evans SJW. Detection of Fraud. In: Armitage P, Colton T, eds. Encyclopedia of Biostatistics. Chichester: John Wiley & Sons; 1998: 1583–1588.Google Scholar
  11. 11.
    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.Google Scholar
  12. 12.
    Mosimann JE, Wiseman CV, Edelman RE. Data fabrication: Can people generate random digits? Account Research. 1995;4:31–55.CrossRefGoogle Scholar
  13. 13.
    Overall JE, Gorham DR. The Brief Psychiatric Rating Scale. Psychol Rep. 1962;10:799–812.CrossRefGoogle Scholar
  14. 14.
    Chernoff H. The use of faces to represent points in k-dimensional space graphically. J Am Stat Assoc. 1973;68(342):361–368.CrossRefGoogle Scholar
  15. 15.
    S-PLUS. Seattle, WA: Statistical Sciences, Inc; 2000.Google Scholar
  16. 16.
    Preece DA. Distribution of final digits in data. The Statistician. 1981;30(1):31–60.CrossRefGoogle Scholar
  17. 17.
    Newman TG, Odell PL. The Generation of Random Variates. No. 29 of Griffin’s Statistical Monographs & Courses. Stuart, A, ed. London; Griffin; 1971.Google Scholar
  18. 18.
    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.Google Scholar
  19. 19.
    Manly BFJ. Randomization and Monte Carlo Methods in Biology. London: Chapman & Hall; 1991.CrossRefGoogle Scholar
  20. 20.
    Bailey K. Detecting fabrication of data in a multicentre collaborative animal study. Control Clin Trials. 1991;12:741–752.CrossRefGoogle Scholar
  21. 21.
    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.CrossRefGoogle Scholar
  22. 22.
    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.Google Scholar
  23. 23.
    Walsh RC. Systematic measures for the prevention and early detection of investigator fraud. Drug Inf J. 1994;28:1161–1165.CrossRefGoogle Scholar
  24. 24.
    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.CrossRefGoogle Scholar

Copyright information

© Drug Information Association, Inc 2002

Authors and Affiliations

  • Rosemary N. Taylor
    • 1
  • Damian J. McEntegart
    • 2
    Email author
  • Eleanor C. Stillman
    • 3
  1. 1.Statistical Services UnitUniversity of SheffieldSheffieldUK
  2. 2.ClinphoneKnoll LimitedNottinghamUK
  3. 3.Department of Probability & StatisticsUniversity of SheffieldSheffieldUK

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