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Oct 13, 2023 10:00 AM - Oct 13, 2023 2:00 PM

Short Course: Measuring the Quality of Real-World Data (RWD)

This Pre-Conference Short Course is held in conjunction with the Real-World Evidence Conference


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

This Short Course will be offered virtually – Join from anywhere!

The focus of the course will be on quantifying the accuracy, completeness and consistency of Real-World Data (RWD). The course will survey existing frameworks for assessing fitness-for-use of data. Use of such frameworks in Data Quality Assessment (DQA) first requires identifying problematic or potentially problematic data; the course will cover rule, redundancy, distributional, and AI-based approaches to problematic data identification. Particularly impactful types of errors expected with RWD sources will be covered to inform risk-based approaches to DQA. Attendees will leave with a systematic approach to (1) identify data errors common to RWD, and (2) implement use-specific data quality dimensions and measures relevant to RWD used to generate RWE.

Registered attendees for this virtual Short Course will receive access to the course recording for 2 full months post-course! This allows you to remain flexible with your schedule and not worry if you need to step out momentarily. Have a conflict with the dates of the course, but are interested in the content? Register anyway and you will receive access to the recording!

This Short Course is a great follow up to Short Course #1: How Good is Good Enough? Fit-for-Purpose Considerations for RWD/RWE for Regulatory Purposes. If you register for both Short Courses and the Real-World Evidence Conference, you will automatically receive $150 off in your cart!

Learning objectives

Upon completing this course, attendees will be able to:
  • Explain common types of error in RWD and explain the most impactful among them
  • Discuss basic components of ensuring or confirming fitness for use and how they differ between primary and secondary data use
  • Identify the data quality dimensions that often comprise fitness for use of RWD used to generate RWE
  • Identify and define data quality measures relevant to RWD/RWE contexts, and
  • Outline steps needed for study teams to implement data quality measures

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