Event

This webinar is aimed at those who perform or use the results of real-world evidence (RWE) studies. It aims to describe the use of predictive analytics and machine learning for causal inference in the context of the extremes for disease populations: rare diseases  and common conditions such as obesity. Attendees will be able to describe predictive analytics in the RWE space, and in particular,  understand the implications for interpretation in the context of both rare disease and common conditions.

• Describe predictive analytics methods used in RWE studies
• Understand the similarities and differences in applying predictive analytics in rare disease populations versus common condition populations
• Appreciate the implications for interpretation of predictive analytics in RWE studies of the extreme ends of sample sizes, from rare disease to common conditions

Moderator:

Noelle N. Gronroos, PhD, MPH, CPH, Senior Health Economics Researcher, Optum Life Sciences, Eden Prairie, MN, USA

Speaker: 

Nicole Engel-Nitz, PhD, MS, Director, HEOR, Optum Life Sciences, Eden Prairie, MN, USA

Brought to you by: Optum 

Please note: On the day of the scheduled webinar, the first 1000 registered participants will be accepted into the webinar. For those who are unable to attend, or would like to review the webinar at a later date, the full-length webinar recording will be made available at the ISPOR Educational Webinar Series webpage approximately 2 days after the scheduled Webinar.