FDA’s Guidance on Real-World Evidence and Real-World Data – Guidance Regarding Data Sources

In the recently released FDA guidance on real-world evidence (RWE) from real-world datasets (RWD) like electronic health records (EHRs) and medical claims data, which further can be extrapolated for other types of data sources (like Real-world imaging data (RWiD), genetic data, etc.). The assessment of RWD plays a pivotal role in regulatory decision-making for drug and biological products. These data sources are crucial for understanding real-world patient outcomes, treatment efficacy, and safety profiles.

The guidance provides insights for the prudent selection and usage of the data sources under the following headings:

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Relevance of the Data Sources

The guidance suggests that the data sources should be evaluated after the study requirements and outcomes have been finalized rather than the reverse. The data sources should be relevant and reliable to the study. It should be able to provide the required information such as – exposure/treatment, dosage, period, and other information that would be relevant to the outcome and a measure of outcome.

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Data Capture

Since EHRs and claims data weren’t designed for research, they may not capture the information necessary for the study. It is crucial to ensure that the data sources have the necessary information and if not, to find alternatives for obtaining those information. The comprehensiveness and completeness of data capture significantly impact the validity of the study findings.

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Data Linkage and Synthesis

Linking multiple datasets collected from multiple sources can enhance the longitudinal patient journey, providing a comprehensive picture of the patient. This can involve linking datasets collected from multiple sources or even different datasets such as EHRs, claims, imaging datasets, etc. When the required information isn’t available, proxy data may synthesize the missing information. The protocol should describe the methods used for data linkage and synthesis, with validation methodology to be used to reduce biases. It should also specify the potential issues that may crop up due to these methods.

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Data Curation and Transformation

Rigorous curation and transformation processes are necessary to maintain the quality of data. This includes data retrieval methods, minimizing missed data, ensuring traceability, and handling discrepancies. Data curation also involves routine quality checks and ensuring consistency & completeness of data elements. Transforming multiple datasets into a standard data model (CDM) helps with standardization and integration across different RWD sources, enhancing the robustness of the study.

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Quality Assurance and Quality Control

A comprehensive QA / QC plan must be included in the protocol and implemented while evaluating the datasets. Since RWD are usually collected for the purpose of healthcare delivery, there will be discrepancies across various datasets and data sources. Hence, a QA/QC plan is essential to ensure data integrity. This includes automated data quality reports, procedures for handling data discrepancies, and documentation of data management processes. The accuracy of mappings between different coding systems, such as ICD-10 and SNOMED CT, and data handling across countries are critical to maintaining data quality.

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Conclusion

The FDA guidance emphasizes that the study is as good as its sources. Choosing the suitable set of sources, through the right processes is essential for success and for obtaining the necessary information. Even though the guidance was specific toward EHRs and claims data, it is also helpful while selecting other types of RWD such as imaging datasets (RWiD), genetic data, and others.

Owing to the various disadvantages that occur with EHRs and claims data, we at Segmed recommend using other datasets like imaging datasets (RWiD), genetic data, etc. will help the studies. EHRs can have issues reporting inconsistencies and errors, which the raw data can support. Linking EHR and claims data with other datasets can reduce errors and provide better outcomes.

Segmed’s curated fit-for-purpose regulatory grade datasets – multimodal and longitudinal can support your studies. Segmed’s dedicated in-house team of subject matter experts, consisting of medical doctors, RWD/RWE, and regulatory experts, ensures that the datasets delivered are of regulatory grade and can be directly used for study purposes. Our expertise in integrating imaging datasets with other types like EHRs and claims through tokenization, provides a comprehensive and complete picture of the study population.

Get in touch with Segmed to understand our solutions and how they can support your studies.

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