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Innov Clin Neurosci. 2016 Feb 1;13(1-2):27-33. eCollection 2016 Jan-Feb.

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

Author information

1
Dr. Daniel is with Bracket Global, Washington, DC; Dr. Kalali is with Quintiles, San Diego, California; Mr. West is with Innovum Technologies, Las Vegas, Nevada (Mr. West was with ePharma Solutions, Plymouth Meeting, Pennsylvania, during the preparation of this executive summary); Dr. Walling is with Collaborative Neuroscience Network, Long Beach, California; Dr. Hilt is with Forum Pharmaceuticals, Waltham, Massachusetts; Dr. Engelhardt is with Cronos, Lambertville, New Jersey; Dr. Alphs is with Janssen Scientific Affairs, LLC, Titusville, New Jersey; Dr, Loebel is with Sunovion, Fort Lee, New Jersey; Dr. Vanover is with Intra-Cellular Therapies, New York, New York; Dr. Atkinson is with Finger Lakes Clinical Research, Rochester, New York; Dr. Opler is with ProPhase LLC, New York, New York; Dr. Sachs is with Bracket Global, Lexington, Massachusetts; Dr. Nations is with INC Research, Austin, Texas; and Dr. Brady is with inVentiv Health Clinical, Princeton, New Jersey.

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

PMID:
27413584
PMCID:
PMC4896826

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