Risk Based Monitoring for Central Nervous System (CNS) Clinical Trials

Risk based monitoring is now fully supported as a method to optimize clinical trial operations and monitoring by the Food and Drug Administration, European Medicines Agency and Pharmaceuticals and Medical Devices Agency. In this webinar, learn how you can shift clinical monitoring, for central nervous system clinical trials, from frequency-based to proactive, comprehensive risk based monitoring.

Central nervous system clinical trials have a relatively poor success rate, not least because it is difficult to measure their clinical endpoints. Clinical outcome assessments (COAs or scales), and particularly clinician-reported outcomes (ClinROs), are often selected as primary outcome measures in CNS clinical trials. However, a small number of errors in these somewhat subjective instruments can profoundly affect study results.

As such, a standard analytics template for the central risk based monitoring of CNS clinical trials has been built using Spotfire’s uniquely accessible API (application programming interface). Using three key risk indicator (KRI) themes, a standardized clinical data model, a scales rule “engine,” and interactive visualizations, a flexible analytics template has been built with the capability of deploying any CNS indication for any set of scales and with study-specific KRIs. The scales rule engine is a self-service analytics tool, accommodating complex scale summation rules and study-specific KRI requirements without any need for additional programming or template.

Key benefits:

• Get more actionable insights to clinical business users, throughout the organization, by providing unmatched speed and flexibility to address clinical monitoring.
• Uncover trends and patterns that represent risk or opportunity through intuitive visualizations, analytic dashboards, and applications.
• Connect to clinical data sources and include disparate clinical, operational and safety data in your risk monitoring program.
• Enable self-service discovery to drastically reduce reliance on IT and eliminate delay related to data preparation, report building, and spread-sheet version control.

Join in this webinar to learn how risk based monitoring can be used to leverage common clinical data sources, advanced statistical methodologies and cutting-edge visualizations for your central nervous system clinical trials.

Speakers

Michael Phillips, Director, Product Innovation, ICON plc

Dr Michael Phillips currently works in the product innovation team at the Pharmaceutical contract research organisation ICON plc, with responsibility for clinical informatics and advanced analytics. He has been at ICON for 4 years, working mainly in the business intelligence and informatics space and delivering a number of strategic initiatives through TIBCO Spotfire. Before joining ICON, he worked as a systems manager for an international publisher of abstracts and indexes, and before that he worked as a general science and medicine editor. He has a PhD in the molecular enzymology of drug detoxification. He believes that self-service informatics is a difficult but absolutely essential challenge in today’s culture of “big data” and information on demand. He has recently published a book on Spotfire called “TIBCO Spotfire: A comprehensive primer,” published by PACKT Publishing.

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Gerard Conway, Director, Data Analytics, PerkinElmer Informatics

Gerard has spent the past 15 years working in Life Science informatics across the full range of Life Science functions including Research, Clinical and Commercial. Gerard recently joined PerkinElmer to help with the application of the TIBCO Spotfire platform in Clinical. Prior to joining PerkinElmer he spent 10 years working for TIBCO where he was a Solutions Consultant Manager working with numerous global Pharmaceutical companies and CROs on Clinical projects, including Medical Review, Clinical Operations and Risk Based Monitoring. Gerard has a BSc in Biochemistry from National University of Ireland Galway and an MSc in Bioinformatics from the University of Manchester.

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