David Knepper1, Christian Fenske2, Patrick Nadolny3, Alun Bedding4, Elena Gribkova5, John Polzer6, Jennifer Neumann7, Brett Wilson8, Joanne Benedict9, Andy Lawton10. 1. 1 Business Operations, Allergan, Jersey City, NJ, USA. 2. 2 Clinical Risk Management, Eli Lilly and Company, Indianapolis, IN, USA. 3. 3 Clinical Programming and Performance Management, Bioinformatics Operations and Systems, Allergan, Irvine, CA, USA. 4. 4 Biostatistics, Roche Products, Welwyn Garden City, Hertfordshire, UK. 5. 5 Clinical Trial Support and Compliance, Pfizer, Moscow, Moscow, Russia. 6. 6 Global Statistical Sciences, Eli Lilly and Company, Indianapolis, IN, USA. 7. 7 Clinical Data Management, Roche Group, Genentech, South San Francisco, CA, USA. 8. 8 Global Development Operations, Research and Development, Bristol-Myers Squibb, Princeton, NJ, USA. 9. 9 Global Product Development, Roche, Genentech, San Francisco, CA, USA. 10. 10 Biometrics and Data Management, Boehringer Ingelheim, Bracknell, UK.
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.
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.