Best Practices in Defining and Aggregating Study Data for Oversight

Clinical Trials, Life Sciences, Pharmaceutical,
  • Wednesday, May 23, 2018

Join Linda Sullivan of Metrics Champion Consortium and Comprehend for an insightful discussion on recent metrics research that uncovered how various aggregation styles and views of study-level data can be used to monitor portfolio-level plans and timelines. The webinar will discuss both advantages and disadvantages of various study data views, and ways in which sponsors can evaluate study data collaboratively with their vendor partners.

Additionally, attendees will see a live demonstration of how leading sponsors can easily aggregate and manage study data using purpose-built automation. With real-time, accurate dashboards and metrics, sponsors can increase their speed to a quality result.

Speakers

Julie Peacock, Client Services, Comprehend

Closely aligned with customers and the sales organization, Julie focuses on enabling prospects and customers on Comprehend’s Clinical Intelligence solutions. She manages go to market strategy, sales enablement, and product marketing.

Prior to Comprehend, Julie spent 18 years at Oracle Corporation in the enterprise application space where she managed strategy, field enablement and launch activities for a series of BtoB solutions. Julie holds a bachelor’s degree in Marketing from Auburn University.

Message Presenter

Linda Sullivan, Co-Founder & President, Metrics Champion Consortium (MCC)

Ms. Sullivan is Co-Founder & President of the Metrics Champion Consortium (MCC), an industry association dedicated to leading the drug-development enterprise in the adoption and utilization of standardized metrics and benchmarks to drive performance improvement. She has been a featured speaker at Performance Metrics, Risk-Based Monitoring, Quality Management & Clinical Trial Oversight industry meetings.

Ms. Sullivan received a B.S. in Biology from Trinity College and an M.B.A. from Dartmouth College where she was named a Tuck Scholar.

Message Presenter

Who Should Attend?

CROs, Clinical Operations and Data Management Professionals

  • Clinical Trial/Clinical Study Management
  • Clinical Data/Informatics/IT
  • Clinical Outsourcing

Clinical Research, Technology and Business Professionals

  • Biometrics/Biostatistics
  • Business Technology/Applications/Solutions
  • Business Analyst
  • CTO
  • Project Management

What You Will Learn

  • Recent metrics research on how various aggregation styles and views of study-level data can be used to monitor portfolio-level plans and timelines
  • Advantages and disadvantages of various study data views
  • Ways in which sponsors can evaluate study data collaboratively with their vendor partners

Xtalks Partners

Comprehend

Comprehend offers a suite of Clinical Intelligence applications that enables ClinOps Execs, Data Managers and Medical Monitors to significantly improve the speed, safety and quality of a portfolio of clinical trials.  Across studies, sites, systems and CROs, Comprehend’s Clinical Intelligence Suite is particularly effective for centralized monitoring, risk monitoring, CRO oversight and collaboration, and medical monitoring initiatives. Comprehend gives life sciences companies a new source of competitiveness and the confidence to deliver high quality trial submissions at a new speed. Comprehend: The speed to quality results. Learn more at www.comprehend.com.

MCC

Founded in 2006, MCC is the leading industry association dedicated to the development of standardized performance metrics to improve clinical trials. MCC provides the collaborative environment for biopharmaceutical and device sponsors, service providers and sites to improve clinical-trial development through use of MCC standardized performance metrics. Based on MCC membership requests, MCC has built a Benchmarking Database and reporting tool for use with MCC’s metrics portfolio. Members who choose to participate in the MCC Benchmarking Database can load in their own MCC metrics data, can track their own performance over time, and compare their data to aggregated, anonymized data from their peers.

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