It’s no secret that data quality is at the heart of every clinical trial. Data validation is one of the main objectives in the FDA’s pre-approval inspection (PAI), which is performed to contribute to the FDA’s assurance that all submitted data are accurate and complete.
For example, in clinical trials where patients self-administer diaries to record important trial information, such as outcomes or intake of medication, there may be a risk of diary falsification. And for trials including a large number of patients spread across numerous sites, it becomes harder to validate data; especially if the Source Data Verification(SDV) method is utilized. This leaves many sponsors and CROs wondering how they can accurately validate data and ensure that their house is in order in preparation for regulatory submission.
A recent paper published by TransCelerate BioPharma (Statistical Monitoring in Clinical Trials: Best Practices for Detecting Anomalies Suggestive Fabrication or Misconduct, Therapeutic Innovation & Regulatory Science 2016, Vol. 50(2) 144-154) suggests that traditional site-monitoring techniques are not an optimal vehicle for identifying data fabrication and other issues that may impact the overall trial results, and that a more reliable option would be to implement a centralized statistical monitoring approach to compliment traditional site monitoring. The authors write:
“Traditional site-monitoring techniques are not optimal in finding data fabrication and other nonrandom data distributions with the greatest potential for jeopardizing the validity of study results. TransCelerate BioPharma conducted an experiment testing the utility of statistical methods for detecting implanted fabricated data and other signals of noncompliance. Methods: TransCelerate tested statistical monitoring on a data set from a chronic obstructive pulmonary disease (COPD) clinical study with 178 sites and 1554 subjects. Fabricated data were selectively implanted in 7 sites and 43 subjects by expert clinicians in COPD. The data set was partitioned to simulate studies of different sizes. Analyses of vital signs, spirometry, visit dates, and adverse events included distributions of standard deviations, correlations, repeated values, digit preference, and outlier/inlier detection. An interpretation team, including clinicians, statisticians, site monitoring, and data management, reviewed the results and created an algorithm to flag sites for fabricated data. Results: The algorithm identified 11 sites (19%), 19 sites (31%), 28 sites (16%), and 45 sites (25%) as having potentially fabricated data for studies 2A, 2, 1A, and 1, respectively. For study 2A, 3 of 7 sites with fabricated data were detected, 5 of 7 were detected for studies 2 and 1A, and 6 of 7 for study 1. Except for study 2A, the algorithm had good sensitivity and specificity (>70%) for identifying sites with fabricated data. Conclusions: We recommend a cross- functional, collaborative approach to statistical monitoring that can adapt to study design and data source and use a combination of statistical screening techniques and confirmatory graphics.”
This statement is not surprising given that TransCelerate has advocated Central Statistical Monitoring to perform Risk-Based Monitoring, and this paper validates some of the monitoring recommendations made in earlier TransCelerate papers.
At CluePoints, we’ve spent over ten years perfecting a software to give sponsors peace of mind when it comes to identifying and mitigating data quality issues in clinical trials. Aligned with the FDA, EMA, and ICH E6 guidance, CluePoints’ Central Monitoring Platform employs a set of unique statistical algorithms to support a Risk-Based Monitoring strategy. These algorithms are embedded in the SMART Engine, a powerful cloud-based software that evaluates how the data from every centre or patient differs from the data across all centres/patients.
Learn how CluePoints SMART Engine detected fraudulent reporting in a large Phase III cardiovascular study
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