Disruptive Innovations in Data Analytics in CNS Clinical Trials

Life Sciences, Clinical Trials, Pharmaceutical Regulation,
  • Wednesday, July 20, 2016

Measurement error and data recording errors (including data fabrication) (Baigent, 2008) are frequent and often non-randomly distributed in clinical trial datasets. Even when accumulated at a single site or a small number of sites their impact may be devastating to the success of a CNS clinical trial. It is therefore critical to identify those sites and raters who are at greatest risk of providing erroneous or otherwise compromised data.

Central statistical monitoring techniques have been shown to successfully identify fraudulent/fabricated data in clinical trials using objective outcomes (Knepper, 2016) but have been less effective in clinical trials using subjective outcome measures such as psychiatric rating scales (O’Kelly, 2004). It is however exactly these ‘subjective outcome’ trials that are in the greatest need of effective data quality monitoring programs.

At Bracket, we have successfully implemented risk-based data quality monitoring programs (Blinded Data Analytics) in a number of CNS clinical trials across numerous indications. These programs combined near real time central statistical monitoring approaches with comprehensive clinical review and where indicated targeted follow-up with the identified raters/sites. In many of these CNS clinical trials, central statistical monitoring worked in concert with intelligent eCOA and remote audio/video monitoring programs.

In this webinar, we will discuss examples of identified data quality concerns coming from CNS clinical trials and the impact of their presence on study outcomes. We will show that many of the data quality concerns are clustering at a small number of centers and many post-randomization data quality concerns can be predicted by the occurrence of similar concerns in the screening period. We will conclude by demonstrating the positive impact of implementing eCOA and remote audio/video monitoring programs on data quality.

Speakers

Alan Kott, MUDr, Clinical Vice President and Practice Lead, Data Analytics, Bracket

MUDr. Alan Kott is Bracket’s Practice Leader for Data Analytics. Having led the development of the Data Analytics Program at Bracket he has overseen the design and reporting of this program in numerous studies across indications. For the past 10 years, he has also provided training to investigators as an Expert Trainer. Dr Kott has been responsible for the design and implementation of multiple in-study data quality programs and served as a Product Business Owner for Rater Reliability Statistical Measures Application. Prior to Bracket, Dr. Kott worked as an Assistant Professor at Charles University, 1st Medical Faculty, Department of Psychiatry, and as a house officer in Psychiatry at General Teaching Hospital, Department of Psychiatry, both in Prague, Czech Republic. Dr. Kott has a Medicinae Universae Doctor (MUDr.) from Charles University.

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David G. Daniel, MD, Senior Vice President and Chief Medical Officer, Bracket, and Professor of Psychiatry, George Washington University

Dr. Daniel is a Bracket’s Senior Vice President and Chief Medical Officer, and a Professor of Psychiatry at George Washington University. He has over 25 years’ experience in supervising and conducting rater training internationally in multi-site industry, government and academic settings. Dr. Daniel has led all Bracket Endpoint Reliability programs in schizophrenia for the past 10 years as Bracket has developed and fine-tuned these programs in support of clinical trials.

Dr. Daniel was the founder and President of Global Learning, LLC which was acquired by United BioSource Corporation in July 2006. Dr. Daniel has formerly served as Medical Director of the NIMH Neuroscience Center at Saint Elisabeth’s where he supervised all clinical research. The primary clinical mission of this intramural NIMH facility was characterization of the anatomical, physiologic and behavioral deficits of schizophrenia and exploration of novel therapeutics for treatment of the cognitive deficits and negative symptoms of schizophrenia. Subsequently Dr. Daniel served as the Director of Clinical Trials for the Stanley Foundation where he supervised over 100 proof of concept clinical trials in schizophrenia and bipolar disorder. Dr. Daniel also was a founding principal of Best Practice, LLC, a Bethesda Maryland based think tank specializing in novel approaches to CNS trials. He has published numerous scientific articles involving clinical trials and investigations of the psychopathology, treatment and evaluation of schizophrenia. He has received patent protection for new treatment approaches he invented in epilepsy, anxiety disorders and motor side effects of antipsychotic treatment.

Dr. Daniel graduated Phi Beta Kappa and Magna Cum Laude from Emory University and obtained his medical school and psychiatry post-graduate training at Vanderbilt University where he served as chief resident. He was a medical staff fellow and senior staff fellow within the intramural program of the National Institute of mental Health (DIRP, NIMH) for 5 years.

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Who Should Attend?

  • Medical/clinical personnel, executives, contract managers and project managers for pharma/biotech companies and CROs
  • Site investigators and raters
  • Academic and other parties invested in clinical trial success

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Bracket

Bracket, with seven offices and more than 500 employees worldwide, is a specialty services provider dedicated to helping bioPharmaceutical sponsors and contract research organizations increase the power of their clinical research data by leveraging core competencies in Science, Technology, and Service. Bracket eCOA™ is a flexible platform for electronic clinical outcomes assessments. Bracket RTSM™ is a best-in-breed, scalable and configurable clinical IRT solution for the life sciences industry. Bracket Rater Training and Quality Assurance improve outcomes through customized training and quality assurance programs. Learn more about Bracket at https://www.bracketglobal.com/

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