Abstract
Background
Data quality issues in clinical trials can be caused by a variety of behaviors including fraud, misconduct, intentional or unintentional noncompliance, and significant carelessness. Regardless of how these behaviors are defined, they may compromise the validity of the study results. Reliable study results and quality data are needed to evaluate products for marketing approval and for decisions that are made on the use of medicine. This article focuses on detecting data quality issues, irrespective of origin or motive. Early detection of data quality issues are important so that corrective actions taken can be implemented during the conduct of the trial, recurrence can be prevented, and data quality can be preserved.
Methods
A survey was distributed to TransCelerate member companies to assess current strategies for detecting and mitigating risks involving fraud and misconduct in clinical trials. A review of literature across many industries from 1985 to 2014 was conducted using multiple platforms.
Results
Eighteen TransCelerate member companies anonymously responded to the survey. All of the respondents had one or more existing strategies for fraud and misconduct detection. The literature search identified current practices and methodologies across many industries.
Conclusions
TransCelerate recommends the creation of an integrated, multifaceted approach to proactively detect data quality issues. Detection methods should include a strategy tailored to the characteristics of the study. Some sponsors are taking advantage of more advanced methods and integrated processes and systems to proactively detect and address issues, relying on advances in technology to more efficiently review data in real time. Further research is underway to assess statistical data quality detection methodology in clinical trials.
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References
Mulinde J. The clinical trial enterprise: defining quality. Paper presented at: DIA Quality Risk Management Conference; December 3, 2012; Philadelphia, PA.
Meeker-O’Connell. Update on clinical trials transformation initiative (CTTI) quality-by-design project. Paper presented at: DIA Quality Risk Management Conference; December 3, 2012; Philadelphia, PA.
Draft guidance on reporting information regarding falsification of data. Fed Regist. 2010;75(33):7412–7426.
United Kingdom Medicines and Healthcare Products Regulatory Agency. Guidance for notification of serious breaches of GCP or the trial protocol, Version 5, Final 060114. 2014.
Hamrell MR. Raising suspicions with the Food and Drug Administration: detecting misconduct. Sci Eng Ethics. 2010;16(4):697–704.
DeMets DL. Distinctions between fraud, bias, errors, misunderstanding, and incompetence. Control Clin Trials. 1997;18(6):637–650.
Al-Marzouki S, Roberts I, Marshall T, Evans S. The effect of scientific misconduct on the results of clinical trials: a Delphi survey. Contemp Clin Trials. 2005;26(3):331–337.6.
Weir C, Murray G. Fraud in clinical trials. Significance. 2011;8(4):164–168.
Buyse M, George SL, Evans S, et al. The role of biostatistics in the prevention, detection and treatment of fraud in clinical trials. Stat Med. 1999;18(24):3435–3451.
Subelj L, Furlan S, Bajec M. An expert system for detecting automobile insurance fraud using social network analysis. Expert Syst Appl. 2011;38(1):1039–1052.
Morley N, Ball LJ, Ormerod TC. How the detection of insurance fraud succeeds and fails. Psychol Crime Law. 2006;12(2):163–180.
Li J, Huang KY, Jin J, Shi J. A survey on statistical methods for health care fraud detection. Health Care Manage Sci. 2008;11(3):275–287.
Kirkwood AA, Cox T, Hackshaw A. Application of methods for central statistical monitoring in clinical trials. Clin Trials. 2013;10(5):783–806.
Steen RG. Retractions in the scientific literature: is the incidence of research fraud increasing? J Med Ethics. 2011;37:249–253.
Seife C. Research misconduct identified by the US Food and Drug Administration. JAMA Intern Med. 2015;175(4):567–577.
Steen RG. Retractions in the medical literature: who is responsible for scientific integrity? Am Med Writ Assoc J 2011;26:2–7.
Wei W, Li JJ, Cao LB, Ou YM, Chen JH. Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web. 2013;16(4):449–475.
Seifert JW. Data mining and the search for security: challenges for connecting the dots and databases. Gov Inf Q. 2004;21(4):461–480.
Lindblad AS, Manukyan Z, Purohit-Sheth T, et al. Central site monitoring: results from a test of accuracy in identifying trials and sites failing Food and Drug Administration inspection. Clin Trials. 2014;11(2):205–217.
Venet D, Doffagne E, Burzykowski T, et al. A statistical approach to central monitoring of data quality in clinical trials. Clin Trials. 2012;0:1–9.
United States 21 CFR §312.70 b-e.
Crimes and Criminal Procedure. 18 USC §1001.
Pogue JM, Devereaux PJ, Thorlund K, Yusuf S. Central statistical monitoring: detecting fraud in clinical trials. Clin Trials. 2013;10(2):225–235.
Baigent C, Harrell FE, Buyse M, Emberson JR, Altman DG. Ensuring trial validity by data quality assurance and diversification of monitoring methods. Clin Trials. 2008;5:49–55.
O’Kelly M. Using statistical techniques to detect fraud: a test case. Pharm Stat. 2004;3(4):237–246.
Wu X, Carlsson M. Detecting data fabrication in clinical trials from cluster analysis perspective. Pharm Stat. 2011;10(3):257–264.
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Knepper, D., Fenske, C., Nadolny, P. et al. Detecting Data Quality Issues in Clinical Trials: Current Practices and Recommendations. Ther Innov Regul Sci 50, 15–21 (2016). https://doi.org/10.1177/2168479015620248
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DOI: https://doi.org/10.1177/2168479015620248