Data Quality Monitoring in Clinical Trials: Has It Been Worth It? An Evaluation and Prediction of the Future by All Stakeholders

Abstract

This paper summarizes the results of the CNS Summit Data Quality Monitoring Workgroup analysis of current data quality monitoring techniques used in central nervous system (CNS) clinical trials. Based on audience polls conducted at the CNS Summit 2014, the panel determined that current techniques used to monitor data and quality in clinical trials are broad, uncontrolled, and lack independent verification. The majority of those polled endorse the value of monitoring data. Case examples of current data quality methodology are presented and discussed. Perspectives of pharmaceutical companies and trial sites regarding data quality monitoring are presented. Potential future developments in CNS data quality monitoring are described. Increased utilization of biomarkers as objective outcomes and for patient selection is considered to be the most impactful development in data quality monitoring over the next 10 years. Additional future outcome measures and patient selection approaches are discussed.

Keywords: CNS; Data quality; clinical trial methodology; clinical trials; data monitoring; drug development; trial design.

FIGURE 1.

Current level of satisfaction

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Area of most critical impact

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Positive or negative impact?

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Eligibility review

FIGURE 5.

Bipolarity index (BPI) metrics table

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Scoring variance between clinicians

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Outcome data monitoring

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Independent clinical review

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Data accuracy

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Data quality monitoring: decrease errors over time

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Data quality monitoring: across scale consistency

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Blinded data analytics

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Site outlier

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Example of a difference with quality data

FIGURE 15.

Data differences with patient samples

FIGURE 16.

Future developments in data quality monitoring