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Real World Evidence
Updated Sept 17

    Real World Evidence

    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

    PHUSE Collaborative Projects

    Real-world Evidence (RWE) in Japan

    Archive

    Real World Evidence Project

    Best Data Practices for Rare Disease Patient Foundations and Researchers

    Real World Evidence Guidance

    Resources

    Requirements and Recommendations for Regulatory Submissions

    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.

    Ashwin.Rai@evidera.com

    With over 15 years of experience in data science across pharma, life sciences and healthcare, Ashwin Rai is a prominent leader in RWE and RWD. As Director of Data Science & Analytics at Evidera, a PPD-ThermoFisher company, Ashwin leads a dynamic team that develops cutting-edge AI algorithms, ML, and NLP models in domains such as RWE, HEOR and PASS studies. His expertise includes implementing ML, NLP and predictive analytics projects – particularly focused on monitoring and management of clinical trials – predictive healthcare modeling, patient segmentation, disease progression analysis, advancing precision medicine, and proactive healthcare management using diverse data sources such as clinical trials, EHR, EMR, labs and pharmacy data. Ashwin also supports Evidera’s Real World Data Solutions team, which contributes to data feasibility assessments, validation, and analysis of third-party real-world data. Ashwin’s leadership has been instrumental in expanding Evidera’s RWE data science business, forging collaborations with clients and experts to deliver innovative, data-driven solutions.

    Ashwin’s commitment to pushing the boundaries of data science for healthcare advancement is further underscored by his abstracts and poster presentations in AI and ML within the healthcare field, including the selection and presentation of his AI prototypes at the prestigious PHUSE/FDA Data Science Innovation Challenge in both 2020 and 2024. These accomplishments highlight Ashwin’s dedication to driving innovation and leveraging data science to address critical challenges in healthcare.

    Ashwin holds a master of science in predictive analytics from Northwestern University and is based in Overland Park, Kansas.

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        • 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
        • Quality and Reusability of Real World Data
        • RWD Guideline for Programming and Analysis Processes
        • Submitting Real World Data
        • Using OMOP and Other Real World Data Standards to Support Regulatory Submissions
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