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Fraud & Misconduct at Investigator Sites. Paul Below Clinical Research Consultant P. Below Consulting, Inc. SoCRA 15 th Annual Conference Chicago, IL September 23, 2006. Disclosure & Disclaimer. I have a consulting relationship with the following companies: MGI Pharma
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Fraud & Misconduct at Investigator Sites Paul Below Clinical Research Consultant P. Below Consulting, Inc. SoCRA 15th Annual Conference Chicago, IL September 23, 2006
Disclosure & Disclaimer • I have a consulting relationship with the following companies: • MGI Pharma • Medical Research Management • Minneapolis Heart Institute Foundation • The views expressed here are my own and I am solely responsible for the content of this presentation
Presentation Topics • Definition of fraud • Prevalence • Famous cases • Consequences • Reasons why fraud occurs • Warning signs/identifiers • Detection strategies • Fraud prevention
FDA Definition of Fraud • Falsification of data in proposing, designing, performing, recording, supervising or reviewing research, or in reporting research results • Falsification includes both acts of omission (consciously not revealing all data) and commission (consciously altering or fabricating data)
Fraud Definition (cont.) • Fraud does not include honest error or honest differences in opinion • Deliberate or repeated noncompliance with the protocol and GCP can be considered fraud, but is considered secondary to falsification of data
Who Commits Fraud? • Investigators • Study coordinators • Data management personnel • Lab personnel • IRB staff • CRAs and sponsor personnel • FDA
Study Coordinator Nurse Hospital Sponsor Investigator Office Staff Sub-investigator CRA Who Gets Blamed? 4% 4% 9% 39% 9% 9% 9% 17% Source: FDA Presentation, DIA 2000
Prevalence of Fraud • Difficult to determine but still considered rare • Reported to significantly impact 1-5% of pharmaceutical clinical trials – F. Wells, Reuters Health, January 2002 • Only ~3% of FDA inspections uncover serious GCP violations resulting in Warning Letters
Famous Cases - Investigators • Robert Fiddes, MDPrivate practice, Whittier, CA – 1997 • Richard Borison, MD and Bruce Diamond, PhD Medical College of Georgia – 1998 • Michael McGee, MDUniversity of Oklahoma, Tulsa – 2000 • Maria Kirkman (aka Ann Campbell), MDPrivate practice, Alabama – 2003
Famous Cases - Coordinators • Anne ButkovitzPediatric private practice, Newton, MA – 2005 • Paul KornakStratton VA Medical Center, Albany – 2005
Robert Fiddes, MD – “Of Mice and Men”, 60 Minutes, April 1, 2001
Bruce Diamond, PhD – “The Lessons of Greed,” PharmaVOICE, July 2004
Paul Kornak – “Abuses Endangered Veterans in Cancer Drug Experiments,” New York Times, February 6, 2005
Consequences of Fraud • Sponsor– data validity compromised, submission jeopardized, additional costs • Investigator– fines, legal expenses, disqualification/debarment, license revocation, incarceration, ruined career • Institution– lawsuits • Subject– safety at risk, loss of trust in clinical trial process
Consequences (cont.) • Fraudulent investigators are often used by multiple sponsors on multiple trials • A small number of investigators can have a broad impact on many NDA submissions • One fraudulent investigator, Dr. Fiddes, was involved in 91 submissions with 47 different sponsors
Why Does Fraud Occur? • Lack of resources (staff, time, subjects) • Lack of GCP training • Lack of regulatory oversight • Laziness • Loss of interest • Pressure to perform or to publish • Money, greed
General Warning Signs • High staff turnover • Staff are disgruntled, fearful, anxious, depressed, defensive • High pressure work environment • Obsession with study payments • Absent investigators • Lack of GCP training • Unusually fast recruitment
Data Identifiers of Fraud • Implausible trends/patterns: • 100% drug compliance • Identical lab/ECG results • No SAEs reported • Subjects adhering perfectly to a visit schedule • Perfect efficacy responses for all subjects
Layout the primary efficacy data for all subjects at a site to look for trends
Data Identifiers (cont.) • Site data not consistent with other centers (statistical outlier) • Source records lack an audit trail - no signatures and dates of persons completing documentation • All source records & CRFs completed with the same pen • Perfect diary cards, immaculate CRFs
Data Identifiers (cont.) • Subject handwriting and signatures are inconsistent across documents (consents, diaries) • Questionable subject visit dates (Sundays, holidays, staff vacations) • Impossible events (eg, subject randomized before IP even available at the site)
Data Identifiers (cont.) • Subject visits cannot be verified in the medical chart or appointment schedule • Data contains “digit preference” – some digits used more frequently than others (0, 5, and even digits) • “Halo” around the date or test value indicating the original was obliterated with correction fluid
Detection Strategies • Expect fraud – start from the assumption that records are bogus and work backwards • Question missing, altered, and/or inconsistent data – offer to retrieve records yourself, keep pulling on loose ends and see what unravels • Don’t be intimidated – challenge to explain suspicious data
Detection Strategies (cont.) • Be suspicious of blame shifting –remind the investigator that he/she is responsible for study conduct • Cultivate whistleblowers – pay attention to staff complaints, listen to grievances, establish rapport, and be approachable
Whistleblowers • Many fraud cases uncovered by staff whistleblowers • Ethical commitment to report fraud (SoCRA): • I recognize my right and responsibility as a clinical research professional to question suspected falsified data, and if necessary, proceed with appropriate reporting procedures as mandated by the appropriate regulatory agencies. • Many institutions have an Office of Compliance with reporting hotlines • US government encourages whistleblowers through False Claims Act awards
False Claims Act • Unlawful to submit a false or fraudulent claim for payment to the United States government • Private citizens who know of people or companies defrauding the government may sue on the government's behalf (qui tam relator) • Plaintiff shares in the proceeds of the suit (15-30% of amount recovered by government) • Contains protections for whistleblowers who are harassed, threatened, discharged or otherwise discriminated against in their employment
Recent False Claims Act settlement with the Mayo Clinic (Rochester, MN)
Cherlynn Mathias - University of Oklahoma Melanoma Trial Whistle Blower
Fraud Prevention • During pre-study evaluation, sponsors should carefully scrutinize sites for interest in the study, stability of the staff, investigator/staff interactions, workload, and level of training • Everyone involved in the clinical trial process should complete regular GCP training • CRAs should be expert on the protocol particularly with parameters that determine eligibility (inclusion/exclusion criteria) and primary efficacy endpoints
Fraud Prevention (cont.) • Sponsors should emphasize their policy on fraud at the initiation visit • Institutions should set-up systems to encourage fraud reporting and protect whistleblowers
This presentation and related references are posted on my corporate website at:www.pbelow-consulting.com/fraud.html
Thanks • Kerrin Young, Study Manager, Takeda, and Jeri Weigand, Quality Assurance Auditor, 3M Pharmaceuticals, for their collaboration in the development of this presentation
Contact Information • Office: (952) 882-4083 • E-mail:paul@pbelow-consulting.com