Medidata AI Research Webinar Series
How Synthetic Control Arms have been used in recent regulatory decisions
Recently we held a series of three webinars about how recent developments in regulatory decision making and these rulings may impact your clinical trial outcome. Listen to the replay of one, two or all three of these sessions as your schedule permits (multiple selections available):
Webinar #1: Celsion – Phase IB Trial Efficacy Estimates via a Clinical Trial Synthetic Control Arm
Abstract: Hear from Elizabeth Lamont, MD, Senior Medical Director at Medidata AI on a study that utilized an external control arm to compare the efficacy of a GEN-1 ovarian cancer drug to a control group who was treated with chemotherapy alone. This comparison helped to show the efficacy of the investigational therapy and helped aid in the decision to continue the trial. The trial drug showed improvement in patients across multiple endpoints. This research was presented in April at the American Association for Cancer Research.
You will learn:
- How an external control arm, such as Medidata Synthetic Control Arm® (SCA), can be used to successfully interpret the results of a single arm study.
- The new, investigational drug, GEN-1, was proven to be more effective in treating late-stage ovarian cancer when combined with the traditional chemotherapy treatment as opposed to chemotherapy alone.
- By successfully evaluating the efficacy of GEN-1 using an SCA, it allowed for fewer patients to be enrolled in the control arm in future phases of the trial, as well as helped to prove that other trials could successfully leverage an SCA to reduce the number of patients enrolled.
Webinar #2: Exploring the Potential of External Control Arms created from Patient Level Data: A case study in non-small cell lung cancer
Abstract: Ruthanna Davi VP, Data Science, Medidata AI discusses the positive impact external control arms have/are expected to have in trials where recruiting a randomized control group is not feasible or ethical. In a NSCLC (non-small cell lung cancer) study, the use of an external control arm showed replicated results to the original study when looking at overall survival when patient level data was matched.
You will learn:
- Synthetic Control Arms (SCA) have been proven to be an effective alternative to a randomized controlled trial when recruiting a control group is not feasible due to ethics, logistics, or the nature of certain indications.
- When the appropriate statistical methods are applied, the historical patient-level data in an SCA can accurately help to measure the effectiveness of the investigational drug in the experimental arm.
- SCAs can be used as a supplemental control arm, an augmented control arm, or they can be used to compare against the original control arm to ensure their accuracy, such as the case with NSCLC.
Webinar #3: Building an External Control Arm for Development of a New Molecular Entity: An Application in a Recurrent Glioblastoma Trial for MDNA55
Abstract: Learn from Ruthanna Davi, VP, Data Science, Medidata AI as she discusses the benefits of using an external control arm in indications such as glioblastoma, where there are no existing successful therapies, and therefore a control group is unethical and difficult to create. Propensity score matching and weighting is key to creating a valid SCA that matches patient level data and allows for an accurate comparison of endpoints in the control and experimental groups.
You will learn:
- Lessons from the regulatory interactions that provide helpful suggestions on how to advance ECA-based drug development.
- About a hybrid trial design using a propensity score weighting method and describe how a standard propensity score analysis can be used to analyze data.
Who should attend:
- Biostatisticians/Biometrics/Data Scientists/Bio-analytics individuals
- R&D Leads
- Chief Medical Officer
- Heads of Early Phase Trials
- Heads/VPs of Pharmacovigilance or Epidemiology
- Heads of Regulatory
- Therapeutic Area Leads
|Elizabeth Lamont, MD
Senior Medical Director, Medidata AI
Dr. Lamont is a physician-scientist with methodologic and analytic expertise in the generalizability of clinical trial results to understudied populations. At Medidata, she works in the Acorn AI team primarily supporting the synthetic control arm team.
Vice President, Data Science, Medidata AI
Ruthanna Davi has a background in pharmaceutical clinical trials with more than 20 years working at the FDA, most recently as a Deputy Division Director in the Office of Biostatistics in CDER. At Medidata Ruthanna is part of a team creating analytical tools to improve the efficiency and rigor of clinical trials. Her recent work is focused on creation and analysis of synthetic or external controls. Ruthanna holds a Ph.D. in Biostatistics from George Washington University.