Real-world evidence (RWE) studies that utilize existing healthcare data to evaluate treatment effects are subject to multiple sources of bias. While confounding and data quality are often considered the major challenges in RWE generation, it has been shown that many observational studies yield biased results because of self-inflicted and avoidable errors in study design. 

Learning Objectives:  

• Discuss major sources of avoidable bias in RWE studies of treatment effects. 
• Describe strategies that will help the audience identify such bias. 
• Discuss current approaches to avoiding self-inflicted bias when designing RWE studies.  

Target Audience: Any are welcome. 

Presenter: Katsiaryna Bykov, Brigham and Women’s Hospital and Harvard Medical School  

Moderator: Mugdha Gokhale, Pfizer