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Interactive statistical monitoring to optimize review of potential clinical trial issues during study conduct
David Pau1, Marie Lotz1, Gaëlle Grandclaude1
1Roche SAS, Boulogne, Billancourt, France.
Background:
Statistical monitoring involves the review of prospective study data collected in participating sites to detect intra/inter patients and sites inconsistencies. We report methods and results of statistical monitoring in a phase IV clinical trial.
Method:
PRO-MSACTIVE is a study evaluating ocrelizumab in active relapsing multiple sclerosis (RMS) patients in France. Specific statistical methods (volcano plots, mahalanobis distance, funnel plot …) have been applied to a SDTM database to detect potential issues. R-Shiny application was developed to generate an interactive web application in order to ease site and/or patients identification during statistical data review meetings.
Results:
The PRO-MSACTIVE study enrolled 422 patients in 46 centers between July 2018 and August 2019. Three data review meetings were held between April and October 2019 and 14 standard and planned tests were run on study data, with a total of 15 (32.6%) sites identified as needing review or investigation. Overall 36 findings were identified during the meetings: duplicate records, outliers, inconsistent delays between dates.
Conclusion:
Statistical monitoring is useful to identify unusual or clustered data patterns that might be revealing issues that could impact the data integrity and/or may potentially impact patients' safety. With anticipated and appropriate interactive data visualization, early signals can easily be identified or reviewed by the study team and appropriate actions be set up and assigned to the most appropriate function for a close follow-up and resolution. Interactive statistical monitoring is time consuming to initiate using R-Shiny, but is time saving after the 1st data review meeting (DRV).(ClinicalTrials.gov identifier: NCT03589105; EudraCT identifier: 2018-000780-91).
Insights
Statistical monitoring of clinical trial data identified issues in 32.6% of sites, including duplicate records and outliers. This process helps ensure patient safety and data integrity in studies like PRO-MSACTIVE.
Area of Science:
- Clinical trial methodology
- Data management and analysis
- Pharmacovigilance
Background:
- Statistical monitoring is crucial for detecting inconsistencies in clinical trial data across patients and sites.
- This process aids in identifying potential issues that could impact data integrity and patient safety.
Purpose of the Study:
- To report the methods and results of statistical monitoring applied in a Phase IV clinical trial (PRO-MSACTIVE).
- To evaluate the effectiveness of statistical monitoring in identifying data inconsistencies and potential patient safety concerns in a real-world study setting.
Main Methods:
- Application of specific statistical methods (e.g., volcano plots, Mahalanobis distance, funnel plots) to a Standard Data Tabulation Model (SDTM) database.
- Development of an R-Shiny interactive web application to facilitate the identification of sites and patients during data review meetings.
- Enrollment of 422 patients across 46 centers for the PRO-MSACTIVE study.
Main Results:
- Three data review meetings were conducted, involving 14 standard and planned tests on study data.
- A total of 15 (32.6%) participating sites were identified for review or investigation.
- 36 findings were noted, including duplicate records, outliers, and inconsistent date entries.
Conclusions:
- Statistical monitoring effectively identifies unusual data patterns, signaling potential data integrity or patient safety issues.
- Interactive data visualization aids in early signal detection and facilitates timely intervention and resolution.
- While initial setup of interactive tools like R-Shiny can be time-consuming, it offers long-term time savings in data review processes.
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