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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 

Quality and Reusability of Real-world Data

Real World Evidence Guidance

Submitting Real-world Data 

Archive

Real World Evidence Project

Berber Snoeijer: Working Group Lead 

b.snoeijer@clinline.eu

Berber Snoeijer started in clinical research in 1997 as a biometrician and has since then worked with clinical data in different functions. In 2001 she started a CRO – Biometric Support – aimed at the data management, data analysis and reporting of clinical trials. In 2011 she started as an R&D manager dedicated to investigating and utilising the potential of real-world data from electronic health records. This resulted in many different solutions including a full reporting system to give feedback information to clinical research professionals. Berber is experienced with software and database engineering, process engineering and improving efficient utilisation and interaction of people based on management drivers. Nowadays, she uses these skills and knowledge to help life science companies assess, design and improve business solutions and processes at smaller and larger scales.

David Hood: Working Group Lead 

David.Hood@axtria.com

David is a manager of Real-world Data (RWD) Programming and Statistics with over 7 years of experience generating Real World Evidence (RWE) for the pharmaceutical industry. He is skilled in big data analytics, business intelligence, and developing innovative RWE methodologies. David has experience analysing claims and EHR data from various sources, and is proficient in SAS, Python, R, Tableau, and predictive analytics. He has contributed to cross-functional RWE generation activities and led internal programming teams to execute deliverables. David has an MS in Business Analytics and SAS Business Analytics Certificate from Quinnipiac University, an MS in Finance from Southern New Hampshire University, and a Bachelor’s degree in Finance from Seton Hall University.

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