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Detecting Data Quality Issues in Clinical Trials: Current Practices and Recommendations.
David Knepper1, Christian Fenske2, Patrick Nadolny3
11 Business Operations, Allergan, Jersey City, NJ, USA.
Therapeutic Innovation & Regulatory Science
|September 22, 2018
Summary
Detecting data quality issues in clinical trials is crucial for reliable results. TransCelerate recommends a proactive, multifaceted approach to identify and mitigate risks like fraud and misconduct early.
Area of Science:
- Clinical trial data management
- Regulatory science
- Data integrity in research
Background:
- Data quality issues in clinical trials, stemming from fraud, misconduct, or noncompliance, can compromise study validity and impact medical product approvals.
- Ensuring reliable study results and high-quality data is essential for informed decision-making regarding medication use and market authorization.
- Early detection of data quality problems allows for timely corrective actions, prevention of recurrence, and preservation of data integrity throughout the trial.
Purpose of the Study:
- To assess current strategies employed by TransCelerate member companies for detecting and mitigating risks associated with fraud and misconduct in clinical trials.
- To identify existing practices and methodologies for data quality detection across various industries through a comprehensive literature review.
Main Methods:
- A survey was distributed to TransCelerate member companies to gather information on their existing fraud and misconduct detection strategies.
- A literature review was conducted, spanning from 1985 to 2014, to identify current practices and methodologies across multiple industries.
Main Results:
- Eighteen TransCelerate member companies participated in the anonymous survey, with all reporting at least one strategy for detecting fraud and misconduct.
- The literature search provided insights into current practices and methodologies employed in various industries for risk detection and mitigation.
Conclusions:
- TransCelerate advocates for an integrated, multifaceted strategy to proactively detect data quality issues in clinical trials.
- Detection methods should be tailored to specific study characteristics, with an increasing adoption of advanced, real-time data review technologies by sponsors.
- Further research is ongoing to evaluate statistical methodologies for data quality detection within clinical trial settings.
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