On-Demand Webinar | Utilize Real World Evidence for More Efficient Trials in a Regulatory Setting

Recorded on: December 9, 2020

 

As the industry strives to develop personalized medicines, more and more rare diseases emerge. Due to changes in the standard of care or the availability of a new perceived superior therapy, adequately recruiting patients that meet the specific selection criteria and/ or retaining control arm patients can be challenging.

Without adequate recruitment and retention of clinical trial participants for a randomized controlled trial, the research objective cannot be addressed, and product development can be slowed or stopped. One way to mitigate these challenges and create more efficient clinical development programs is to leverage an external control group built upon historical clinical trial data to replace or augment a control arm in a randomized clinical trial.

This novel approach can be used to enhance scientific decision making in early phase clinical research but is also starting to be used in regulatory settings for some indications. Historical clinical trial data can be useful for a myriad of other objectives, such as providing estimates of control arm efficacy outcomes for sizing of a future trial in a uniquely defined subset of patients or for exploration of prognostic factors to fuel knowledgeable development of eligibility criteria.

Attend this webinar as experts from Acorn AI discuss best practices around external controls and making data driven trial design decisions in a regulatory setting.

LEARNING OBJECTIVES

Attend this webinar to learn how external control groups have been used to:

  • Improve participant recruitment and minimize effect of differential drop-out on treatment effect estimation.
  • Augment RCT to enable more efficient randomized clinical trials in indications where the standard of care therapy is impracticable or unacceptable to patients.
  • Efficiently design eligibility criteria for a future trial with understanding of baseline factors associated with efficacy or safety outcomes (e.g., large changes in efficacy outcomes over the expected study timeline or occurrence of severe adverse events have occurred in particular subsets of patients in previous trials).

SPEAKERS

Ruthanna Davi, PhD
Vice President, Data Science, Acorn AI, a Medidata company

Ruthie Davi is a Statistician and Vice President, Data Science at Acorn AI, a Medidata company, and has a background in pharmaceutical clinical trials with more than 20 years working as a Statistical Reviewer, Team Leader, and Deputy Division Director in the Office of Biostatistics in CDER at FDA. At Acorn AI Ruthie is part of a team creating analytical tools to improve the efficiency and rigor of clinical trials, an example of which is her Synthetic Control Arm work. Ruthie holds a PhD in Biostatistics from George Washington University.

Jacob Aptekar, MD, PhD
Senior Director, Trial Design Solutions Lead, Acorn AI, a Medidata Company

Jacob leads the Trial Design Solutions group within Acorn AI, focusing on novel data science applications for Medidata's clinical trial and real-world data. Prior to joining Medidata he was an Associate at McKinsey & Company within their Digital McKinsey group. Prior to that, he founded and led Qurator, Inc, a start-up data science consultancy focused on RWE and renal disease. He holds an MD and PhD in Neuroscience from UCLA and an AB in Physics from Harvard.


ABOUT THE SPONSOR

Acorn AI™ is a Medidata company that represents the next horizon of the industry leader’s 20-year mission of powering smarter treatments and healthier people. Acorn AI is designed to make data liquid across the entire lifecycle and to answer the most important questions in R&D and commercialization for customers. Built upon the Medidata platform comprising more than 20,000 trials and 5 million patients, Acorn AI products feature one of the industry’s largest structured, standardized clinical trial data repository connected with real world, translational and other datasets. Learn more at www.medidata.com/en/acornai.



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