Balancing Risk and Reward: How to Pick the Right Spot on the Virtual Trial Spectrum
Sponsored by: Covance
- Virtual Trial Spectrum
Date: 16 December
Days to go: 7
Time: 4PM London/11AM New York
Learn how to leverage the best of traditional, hybrid and virtual trials for your pipeline
As sponsors are increasingly exploring ways to add efficiencies to the drug development process, virtual trials have come into the spotlight along with new supporting technologies. With promises of improved data quality, greater patient centricity, reduced cost and accelerated time to market, there are many potential ways that clinical development can be transformed. However, a virtualized design with the latest technology may not be the right fit for each and every trial. Each potential element for a virtual trial must be evaluated for its overall benefit – and potential risk – to understand the added value based on the unique requirements of the trial.
Sponsors who are exploring the possibility of transitioning from a traditional model to a virtual model – or those that want to incorporate additional virtual elements to their hybrid design – must be able to look beyond the buzzwords and understand the fine balance of risk and reward. To address this challenge, this educational presentation discusses the decision-making process for finding the right mix of traditional and virtual elements and how to evaluate various methods and technologies to determine which could notably improve development by accelerating study timelines, increasing patient retention, reducing patient burden and maintaining data quality – without adding unnecessary obstacles. Platform modularity and data interoperability are essential for streamlined study delivery.
Beyond the use of technology, the presenters will also discuss how to evaluate and identify protocols that are suitable for hybrid virtual trials. You will learn how a hybrid and/or a virtual trial can be specifically customized to a protocol and hear about the potential for extending beyond individual studies to apply key findings to downstream designs and trials.
Register today to learn from these two thought leaders as they share their expertise on this rapidly evolving field.
Bola Oyegunwa, PhD, MBA,
Vice President, Global Head of Hybrid and Virtual Trials, Covance
Dr. Bola Oyegunwa is the Vice President and Head of Hybrid and Virtual trials at Covance. Bola is an industry thought leader in developing and implementing virtual trial solutions. Bola has led the implementation of innovative technology-enabled hybrid and virtual trials solutions for leading biotech and pharmaceutical companies. Bola is a champion of reducing patient burden in clinical trials and regularly engages with regulatory authorities across North America and Europe to remove regulatory barriers to the adoption of virtual clinical trials. Bola is an ex-strategy consultant and has held leadership roles in Corporate Strategy, Product Development and Innovation at leading CROs.
Michelle Longmire, MD,
Michelle Longmire, MD, founded Medable with the vision of making health technology as seamless and as integrated as the human body. With a background in image analysis and processing, she holds patents in machine learning as applied to medical diagnostics. Today, Dr. Longmire serves as the company’s CEO and leads the charge to enable a band of solutions in medical data and integration for more seamless and effective personalized health care delivery and clinical research management. The Medable platform removes the barriers to healthcare innovation to enable the development of life changing technologies efficiently, at scale, and with analytics capabilities that will transform the future of healthcare.
Key Learning Objectives
- Understand the drivers behind adding virtual elements to a traditional trial design
- Learn about the wide range of solutions along the traditional-hybrid-virtual spectrum
- Hear about decision-making processes to evaluate new methods and technologies
- Learn about the potential to extend findings beyond an individual study
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