Real World Evidence – Project Volunteer Board

We’re looking for a Working Group Lead for our Real World Evidence Working Group, which aims to support, address and answer pertinent questions within the industry. 

Working Group Scope

The Real World Evidence Working Group aims to support, address and answer pertinent questions around real-world evidence. The Working Group is dedicated to sharing across the PHUSE Community (through Community Forums) and aligning on the best industry practices. Some of the questions we intend to address are:

• What are the requirements, technologies and processes needed to use real-world evidence as a source for data analysis?

• What are the requirements, technologies and processes needed to incorporate real-world evidence into clinical trials?

• What are the requirements, technologies and best practices needed to support the use of real-world evidence as part of regulatory submissions?

Current Projects

• Applying Advanced Data Privacy Methods to Real World Data (RWD)

• Estimands for RWD/RWE

• Missing Data Imputation in RWD Exploration of Multiple Techniques on Open-Source Data

• RWD Guideline for Programming and Analysis Processes

• Submitting Real World Data 

• Quality and Reusability of Real World Data

• Using OMOP and Other Real World Data Standards to Support Regulatory Submissions

Working Group Lead Roles and Responsibilities 

You can learn about the role and the responsibilities here. If you are interested in this role or have any questions, workinggroups@phuse.global. Closing date: 26 September. 

Project Scope:

We aim to publish a white paper which shall explore multiple models available for missing data imputation and share with the wider group the potential of each model and its efficiency in dealing with different kinds of missing data. The model efficiency is compared using a single open-source simulated dataset. 

Regular Project Meeting Day/Time:

• Monthly: Thursday 14:00-15:00 BST

• Monthly Sub Team Meeting: TBC

Sub Teams:

• Sub Team 1 – Missingness of Data

• Sub Team 2 – Literature Review

Key Skills:

• Programmers/Data scientists: Experience in anyone of the mentioned programming languages – R, Python, SAS.

• Experience with imputation techniques would be of advantage (proc MI, MICE model etc).

• Statisticians who would like to support AI/ML models for imputation (depending on the data points to impute).