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Welcome! |
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Interested in joining one of the Data Transparency Real World Evidence Working Group Projects? The projects below are currently calling for volunteers. |
Submitting Real World Data – Started Q2 2024 | |
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Project Scope: The scope of the project includes developing collaterals for submission of real-world data through mutual discussion, knowledge and experience sharing by industry colleagues. The collaterals will be developed by undertaking research of existing draft guidance documents on real-world evidence by regulatory bodies, and by analysing members’ lessons learnt from completed case studies. The scope of this project will include submission of real-world data obtained from primary and widely used real-world data sources. These may include key electronic health record sources, patient reported outcomes, widely used and accepted claims data sources, and other commonly used observational data. This project will also touch upon the data curation and ingestion processes pertaining to clinical operational as well as submission data. Since real-world data can come from a wide variety of sources, the scope of this project will consider the widely and most commonly used real-world data sources by industry and member companies. | Current Status:
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Quality and Reusability of Real World Data – Started QQ2 2023 | |
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Project Scope: Within this project we will make a list of available data sources and pros and cons. We will discuss what documents and requirements we need to show the FDA in the pre-alignment meeting and we will try to point to or create an outline and/or requirements for those documents. If possible, we will align with the FDA on this. | Current Status:
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Missing Data Imputation in RWD Exploration of Multiple Techniques on Open-Source Data – Started Q2 2024 | |
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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. | Current Status:
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