Qualitative features in clinical trials: coordinates for prevention of passive and active misconduct

Authors

  • José Roberto Wajman Federal University of São Paulo, Sector of Behavioral Neurology. Department of Neurology and Neurosurgery (UNIFESP), São Paulo http://orcid.org/0000-0002-9296-2498
  • Sheilla de Medeiros Correia Marin Federal University of São Paulo, Sector of Behavioral Neurology. Department of Neurology and Neurosurgery (UNIFESP), São Paulo
  • Paulo Henrique Ferreira Bertolucci Federal University of São Paulo, Sector of Behavioral Neurology. Department of Neurology and Neurosurgery (UNIFESP), São Paulo
  • Marcia Lorena Fagundes Chaves Department of Internal Medicine, Faculty of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre
  • Theresa Bromley Global Rater Training Services at ePharmaSolutions (ePS), Philadelphia, PA Clinical Services, MedAvante-ProPhase, New York, NY

DOI:

https://doi.org/10.18203/2349-3259.ijct20180125

Keywords:

Good clinical practice, Clinical trial, Data quality, Research misconduct, Fraud prevention

Abstract

For many years, the quality concept in clinical trials has been discussed and recommended by Good Clinical Practice (GCP) guidelines. Regulatory Authorities and also the Public Involvement anticipate that the pharmaceutical industry will concentrate on creating quality frameworks amid the arranging and leading of conventions of controlled protocols. Nevertheless, many factors have been suggested as contributing to the occurrence of scientific misconduct within the research field, such as: personal and financial interests, site monitoring, available resources, workload, competition among investigators, and the implicit consent of sponsors. The negligence on data fraud represents not only omission but misconduct as well, in this case, a passive attitude intrinsically related to the act of transgression. A properly culture of research must be based on a fundamental ethos of integrity, openness and honest work of high quality in all parts of the research process. There is a need to change the focus from inspection-based quality improvement to planned systematic quality management within clinical trials. In search for a monitoring improvement, a full statistical  way to deal with information recognition comprises of executing however many measurable tests as could be allowed on whatever number clinical information factors as could be expected under varied circumstances. Adoption of specific and preventive clinical trial monitoring procedures can identify potential misconduct and data fraud leading to improvement in overall data quality and scientific reports. 

Author Biography

José Roberto Wajman, Federal University of São Paulo, Sector of Behavioral Neurology. Department of Neurology and Neurosurgery (UNIFESP), São Paulo

Behavioral Neurology Sector. Department of Neurology and Neurosurgery. Affiliate professor.

References

Clinical Trials Transformation Initiative. Available at: http:// www.trialstransformation.org. Accessed on 3 August 2017.

Kleppinger CF, Ball LK. Building Quality in clinical trials with use of a quality systems approach. Clin Infect Dis. 2010;51:S111-6.

US FDA Information Sheet Guidance for IRBs, Clinical Investigators, and Sponsors FDA Inspections of Clinical Investigators. Available at: http://www.fda.gov/downloads/RegulatoryInform-ation/Guidances/ UCM126553.pdf. Accessed on 3 August 2017.

Lou A. Preparing for an FDA Medical Device Sponsor Inspection. Available at: http://www.fda. gov/downloads/Training/CDRHLearn/UCM176843 .pdf. Accessed on 3 August 2017.

Meeker-O’Connell A. Enhancing clinical trial quality: CDER perspective Available at: http://www.fdanews.com/ext/files/Conference/ FIS10Presentations/MeekerOConnellHarmonizingRegulatoryApproaches.pdf. Accessed on 13 August 2017.

Morrison BW, Cochran CJ, White JG, Harley J, Kleppinger CF, Liu A, et al. Monitoring the quality of conduct of clinical trials: A survey of current practices. Clin Trials. 2011;8:342-9.

Jessen J, Robinson E, Bigaj S, Popiolek S, Goldfarb NM. Unreported clinical research fraud and misconduct. J Clin Res Best Pract. 2007;3:1–5.

Rees M, Wells F. Falling research in the NHS. BMJ. 2010;340:c2375.

Research ethics, misconduct and fraud: The Clinical Research Unit 2008 Newsletter Oslo University, Norway.

Benos DJ, Fabres J, Farmer J, Gutierrez JP, Hennessy K, Kosek D, et al. Ethics and scientific publication. Adv Physiol Educ. 2005;29:59-74.

Newton RR. The Crime of Claudius Ptolemy. Johns Hopkins University Press; Baltimore: 1977.

Galton DJ. Did Mendel falsify his data? QJM. 2012;105:215-6.

Fisher RA. Has Mendel’s work been rediscovered? Ann Sci. 1936: 115–137.

Fisher B, Redmond CK. Fraud in breast-cancer trials. N Engl J Med. 1994;330(20):1458–60.

Weir C, Murray G. Fraud in clinical trials: Detecting it and preventing it. Significance. 2011: 164–168.

Horton R. After Bezwoda. Lancet. 2000;18:942–3.

Weiss RB, Rifkin RM, Stewart FM, Theriault RL, Williams LA, Herman AA, et al. High-dose chemotherapy for high-risk primary breast cancer: an on-site review of the Bezwoda study. Lancet. 2000;18:999–1003.

Eichenwald K, Kolata G. A doctor’s drug studies turn into fraud. The New York Times on the Web. 1999: A1–A16.

Swaminathan V, Avery M. FDA enforcement of criminal liability for clinical investigator fraud. Hastings Sci Tech Law J. 2012;4(2):325–56.

Birch DM, Cohen G. How a cancer trial ended in betrayal, 2001. Avaliable at: http://www.balti moresun.com/bal-te.research24jun24-story.html# page=1. Accessed on 3 August 2017.

Grant B. Biotech’s baddies. Scientist. 2009;23:48.

Kranke P, Apfel CC, Roewer N. Reported data on granisetron and postoperative nausea and vomiting by Fujii et al. are incredibly nice!. Anesth Analg. 2000;90:1004.

Carlisle JB. The analysis of 168 randomised controlled trials to test data integrity. Anaesthesia. 2012;67:521-37.

Baggerly KA, Coombe KR. Deriving chemosensitivity from cell lines: forensic bioinformatics and reproducible research in high-throughput biology. Ann Appl Stat. 2009;3:1309–34.

Husten L. Diovan data was fabricated, say Japanese Health Minister and university officials. vhttp://www.forbes.com/sites/larryhusten/2013/07/12. Accessed on 1 August 2017.

Oransky I. Novartis Diovan scandal claims two more papers, 2014. Available at: http://retraction watch.com/2014/04/02/novartis-diovanscandal-claims-two-more-papers/. Accessed on 3 August 2017.

Stroebe W, Postmes T, Spears R. Scientific Misconduct and the Myth of Self-Correction in Science. Perspect Psychol Sci. 2012;7:670-88.

Study site standard operating procedure, Clinical Trial Magnifier, 2010; 3.

Al-Marzouki S, Roberts I, Marshall T, Evans S. The effect of scientific misconduct on the results of clinical trials. Contemp Clin Trials. 2005;26:331-7.

George SL, Buyse M. Data fraud in clinical trials. Clin Investig. 2015;5:161–73.

George SL. Research misconduct and data fraud in clinical trials: prevalence and causal factors. Int J Clin Oncol. 2016;21:15–21.

Fanelli D. How many scientists fabricate and falsify research? A systematic review and meta-analysis of survey data. PLoS ONE. 2009;5:e5738.

Reynolds SM. ORI findings of scientific misconduct in clinical trials and publicly funded research, 1992–2002. Clin Trials. 2004;1:509–16.

Hamilton D. In the trenches, doubts about scientific integrity. Science. 1992;255:1636.

Habermann B, Broome M, Pryor ER, Ziner KW. Research coordinators’ experiences with scientific misconduct and research integrity. Nurs Res. 2010;59:51–7.

Jacobsen G, Hals A. Medical investigators’ views about ethics and fraud in medical research. J.R. Coll Physicians. Lond. 1995;29:405–9.

Ranstam J, Buyse M, George SL, Evans S, Geller NL, Scherrer B, et al. Fraud in medical research: an international survey of biostatisticians. ISCB Subcommittee on Fraud. Controlled Clin Trials. 2000;21:415–27.

Farthing MJG. Research misconduct: diagnosis, treatment and prevention. Br J Surg. 2000;87:1605–9.

Howard E. Science misconduct and due process: a case of process due. Hastings L J. 1993;45:309.

Office of Research Integrity. Case Summaries. Available at: http://ori.hhs.gov/case_summary. Accessed on 6 August 2017.

Retraction Watch. Available at: http://retraction watch.com Accessed on 12 August 2017.

Fang FC, Steen RG, Casadevall A. Misconduct accounts for the majority of retracted scientific publications. PNAS. 2012;109:17028–33.

Getz K. Protocol design trends and their effect on clinical trial performance. Raj Pharma. 2008: 315-6.

Pryor ER, Habermann B, Broome ME. Scientific misconduct from the perspective of research coordinators: a national survey. J Med Ethics. 2007;33:365-9.

Herson J. Strategies for dealing with fraud in clinical trials. Int J Clin Oncol. 2016;21:22–7.

Manheimer E, Anderson D. Survey of public information about ongoing clinical trials funded by industry: evaluation of completeness and accessibility. BMJ. 2002;325:528-31.

Association of American Medical Colleges Task Force on Financial Conflicts of Interest in Clinical Research. Protecting subjects, preserving trust, promoting progress: policy and guidelines for the oversight of individual financial interests in human subjects’ research. Association of American Medical Colleges, Washington DC, 2001.

Wiwanitkit V. Research misconduct and data fraud in clinical trials. Int J Clin Oncol. 2016;21:1196.

Lindblad AS, Manukyan Z, Purohit-Sheth T, Gensler G, Okwesili P, Meeker-O'Connell A, et al. Central site monitoring: Results from a test of accuracy in identifying trials and sites failing Food and Drug Administration inspection. Clin Trials. 2013;11:205–17.

Desmet L, Venet D, Doffagne E, Timmermans C, Burzykowski T, Legrand C, et al. Linear mixed-effects models for central statistical monitoring of multicenter clinical trials. Statistics in Medicine. 2014;33:5265–79.

Valdés-Márquez E, Hopewell CJ, Landray M, Armitage J. A key risk indicator approach to central statistical monitoring in multicentre clinical trials: method development in the context of an ongoing large-scale randomized trial. Trials. 2011;12:A135.

Kirkwood AA, Cox T, Hackshaw A. Application of methods for central statistical monitoring in clinical trials. Clin Trials. 2013;10:783–806.

Venet D, Doffagne E, Burzykowski T, Beckers F, Tellier Y, Genevois-Marlin E, et al. A statistical approach to central monitoring of data quality in clinical trials. Clin Trials. 2012;9:705–13.

Downloads

Published

2018-01-23

Issue

Section

Review Articles