DIAアカウントをお持ちの場合、サインインしてください。

サインイン

ユーザーIDをお忘れですか? or パスワードをお忘れですか?

Renaissance Baltimore Harborplace Hotel

2023年10月13日 (金) 午前 10:00 - 2023年10月13日 (金) 午後 2:00

202 E Pratt Street, Baltimore, MD 21202, USA

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

概要

*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!

学習目的

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

最新情報や機会を逃さないで

DIAのメールを購読すれば、常に最新の業界情報やイベント情報を得ることができます。