How ICH E6(R2) has given clinical development organizations the perfect opportunity to improve quality and lower costs through adoption of RBM and Centralized Monitoring
In this insightful and practical webinar, delegates will learn first-hand how to embrace the paradigm shift that is going on within the industry. No matter the size of your organization – small, mid-size or large – listen to pragmatic advice and see powerful case studies demonstrating the blend of change management and technology that will work for you.
Two industry thought leaders and RBM experts, including Steve Young, COO from CluePoints, will guide the audience through a Risk-Based Approach to managing studies, as advocated by the regulators, from Pre–Study Risk Assessment right through to Database Lock and Submission.
Speakers
Steve Young, Chief Operations Officer, CluePoints
Steve holds a Masters degree in Mathematics from Villanova University. He worked for three biopharmaceutical companies over a span of 15 years, where he assumed leadership positions in clinical data management and led the successful enterprise roll-out of EDC at both J&J and Centocor. He spent an additional 6 years with Medidata and then OmniComm, leading the development of analytics and risk-based monitoring (RBM) solution portfolios and providing RBM and clinical operations consulting to many organizations. He also led a pivotal RBM-related analysis in collaboration with TransCelerate, and has co-authored two patents related to RBM methods.
Message Presenter
Jason Raines, Head, Biometrics Data Sciences and Operations, Biogen
Jason currently serves as the Head, Biometrics Data Sciences and Operations at Biogen. This Biometrics function comprises Clinical Data Management, Statistical Programming, Biometrics Business Performance Management, Data Standards and Governance, Data Analytics and Innovation. Jason has over 22 years’ experience in clinical research. Over the course of his career, Jason has lead the development of best in class Data Management and Statistical Programming organizations and implemented globally-deployed clinical technologies.
Prior to joining Biogen, Jason was Head, Global Data Operations at Novartis, Alcon Division, which included Clinical Data Management, Statistical Programming, Clinical Systems, and Standards. He also served as the R&D IT Portfolio Coordinator responsible for oversight and administration of all R&D IT-related activities. Lastly, Jason served as the Novartis EDC/CDMS Modernization Steering Committee chair and was an active member in Novartis cross-divisional workstreams responsible for developing Digital, Real World Evidence, Big Data, and Clinical Data and Reporting Transformation strategies.
Jason holds Bachelor’s and Masters of Science degrees from the University of Texas, and earned a Masters degree in Biostatistics from the University of North Texas Health Science Center. Jason is a frequent speaker and contributor to Data Management and Statistical Programming-focused conferences.
Message PresenterWho Should Attend?
- Clinical Operations
- Data Management
- Biostatistics
- Quality Assurance
- Regulatory
What You Will Learn
Participants will benefit from the following:
- Advice to determine the right scale of RBM adoption – including a sensible step by step approach
- How to ensure cross-functional adoption of the right approach
- Seeing what others have done in terms of methodology and technology adoption
- An overview of how to improve data quality and determine tangible cost savings through RBM adoption
- The future of RBM – what Sponsors and CROs can expect in the future
Xtalks Partner
CluePoints
CluePoints is a Central Statistical Monitoring solution that employs unique statistical algorithms to determine the quality, accuracy and integrity of clinical trial data. By identifying anomalous data and site errors, corrective actions can be taken ensuring data quality and a reduction in overall regulatory submission risk.
Aligned with guidance from the FDA and EMA, CluePoints is deployed to support traditional on-site monitoring and can be implemented as the engine to drive a risk-based monitoring strategy.
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