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Mar 29, 2022 10:00 AM - Mar 29, 2022 2:00 PM

(US Eastern Standard Time)

Design, Analysis and Evaluation of Confounding Bias Using RWD


*Short Courses require an additional registration fee. You do not need to be registered for the forum to attend*

This Short Course will be offering in a virtual format only

Real-world evidence (RWE) and Real-world data (RWD) are playing an increasing role in health care decisions, especially since the passage of the 21st Century Cure Act in 2016. Due to the increasing use of RWD in the regulatory setting, statisticians have encountered greater challenges in trial design and data interpretation. Some of the issues concerning RWD include understanding data sources, determining data relevancy, consideration of measurement errors, variable balancing techniques, and evaluating confounding bias. Many of these issues may not be routinely encountered with randomized trials, and they are more often handled by the Epidemiology Department. Due to the rising role of RWD in health care, it is important to become familiar with analyzing these data to generate an appropriate and efficient use of RWD and provide constructive advice to clinicians. In this session, Professor Michele Funk, the primary investigator of the DETECTe (Detailing and Evaluating Tools to Expose Confounded Treatment Effects) Demonstration Project, will provide information on data sources, research bias and confounding, and various statistical methods used with RWD.

Learning objectives

At the conclusion of this session, participants should be able to:
  • Identify the pros and cons of different RWD data sources
  • Recognize the different methods used with RWD to minimize bias and evaluate confounding variables
  • Define practical examples using RWD
  • Recognize the applications and software used in RWD
  • Interpret RWD results and recognize their limitations

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