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The Westin San Diego Bayview Hotel

2025年10月09日 (木) 午前 10:00 - 2025年10月09日 (木) 午後 2:00

400 W Broadway, San Diego, CA 92101, USA

Short Course: Getting Started with Estimands in Real-World Evidence Studies

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

概要

The course will discuss the theoretical aspects of estimands as outlined in the ICH E9(R1) addendum, supplemented by hands-on examples to illustrate their use in real-world data contexts. Participants will learn about the five key attributes of estimands: target population, treatment condition, variable or endpoint, population-level summary, and strategies for handling intercurrent events, and understand why they are each crucial for effective study design. Participants will explore the challenges of defining and utilizing estimands, such as choosing analysis methods that most appropriately align with the defined estimand and avoiding misinterpretation. The course will also present solutions to these challenges, ultimately enhancing the robustness and reliability of findings in real-world evidence studies. Moreover, the course will emphasize the importance of using the estimand framework to improve stakeholder communication and align study objectives, design, conduct, statistical analysis, and interpretation.

 

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! Note: You do not need to register for the full RWE Conference to attend this Virtual Short Course.

学習目的

At the conclusion of this course, participants should be able to:
  • Define the concept of estimands and explain their significance in real-world evidence studies
  • Describe how to apply the steps required to define and implement the estimand framework in various real-world data contexts
  • Identify the challenges associated with using estimands and propose solutions to overcome these challenges in real-world evidence studies

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