International Journal of Clinical Oncology

, Volume 21, Issue 1, pp 22–27 | Cite as

Strategies for dealing with fraud in clinical trials

  • Jay HersonEmail author
Review Article


Research misconduct and fraud in clinical research is an increasing problem facing the scientific community. This problem is expected to increase due to discoveries in central statistical monitoring and with the increase in first-time clinical trial investigators in the increasingly global reach of oncology clinical trials. This paper explores the most common forms of fraud in clinical trials in order to develop offensive and defensive strategies to deal with fraud. The offensive strategies are used when fraud is detected during a trial and the defensive strategies are those design strategies that seek to minimize or eliminate the effect of fraud. This leads to a proposed fraud recovery plan (FRP) that would be specified before the start of a clinical trial and would indicate actions to be taken upon detecting fraud of different types. Statistical/regulatory issues related to fraud include: dropping all patients from a site that committed fraud, or just the fraudulent data (perhaps replacing the latter through imputation); the role of intent-to-treat analysis; effect on a planned interim analysis; effect on stratified analyses and model adjustment when fraud is detected in covariates; effect on trial-wide randomization, etc. The details of a typical defensive strategy are also presented. It is concluded that it is best to follow a defensive strategy and to have an FRP in place to follow if fraud is detected during the trial.


Research misconduct Regulatory strategies Clinical trial methodology Fraud recovery plan 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    George SL, Buyse M (2015) Data fraud in clinical trials. Clin Investig (Lond) 5(2):161–173CrossRefGoogle Scholar
  2. 2.
    Goodman A (2006) Intuition. Dial, New YorkGoogle Scholar
  3. 3.
    U.S. Food and Drug Administration (2013) Guidance for industry: oversight of clinical investigation—a risk-based approach to monitoring. U.S. Food and Drug Administration, Silver Spring. Accessed 23 June, 2015
  4. 4.
    Venet D, Doffagnes E, Burzykowski T et al (2012) A statistical approach to central monitoring of data quality in clinical trials. Clin Trials 9(6):705–713CrossRefPubMedGoogle Scholar
  5. 5.
    Fisher B, Redmond CK (1994) Fraud in breast cancer trials. N Engl J Med 330:1458–1459CrossRefPubMedGoogle Scholar
  6. 6.
    Califf RM, Filerman GL, Murray RK et al (2012) The clinical trial enterprise in the United States: a call for disruptive innovation. Discussion paper. Institute of Medicine, Washington, DCGoogle Scholar
  7. 7.
    U.S. Food and Drug Administration (2012) Guidance for industry and investigators: safety reporting requirements for INDs and BA/BE studies. U.S. Food and Drug Administration, Silver Spring. Accessed 23 June 2015
  8. 8.
    Frankel C, Palmieri FM (2010) Lapatinib side-effect management. Clin J Oncol Nurs 14(2):223–233CrossRefPubMedGoogle Scholar
  9. 9.
    Crown JP, Burris HA, Boyle F et al (2008) Pooled analysis of diarrhea events in patients with cancer treated with lapatinib. Breast Cancer Res Treat 112(2):317–325CrossRefPubMedGoogle Scholar
  10. 10.
    Shao J, Zhong B (2003) Last observation carried forward and last observation analysis. Stat Med 22(15):2429–2441CrossRefPubMedGoogle Scholar
  11. 11.
    Little RJA, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Wiley-Interscience, HobokenGoogle Scholar
  12. 12.
    Pocock SJ, Simon R (1975) Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics 31(1):103–115CrossRefPubMedGoogle Scholar
  13. 13.
    Wernicke JF, Faries D, Milton D et al (2005) Detecting treatment-emergent adverse events in clinical trials—a comparison of spontaneously reported and solicited collection methods. Drug Saf 28:1057–1063CrossRefPubMedGoogle Scholar

Copyright information

© Japan Society of Clinical Oncology 2015

Authors and Affiliations

  1. 1.Johns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  2. 2.Chevy ChaseUSA

Personalised recommendations