On July 25, 2024, the U.S. Food and Drug Administration (FDA), through its Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER), and Oncology Center of Excellence, released a comprehensive final guidance titled "Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products." This guidance provides a detailed framework for using real-world data (RWD), particularly electronic health records (EHRs) and medical claims data, to support regulatory decisions.
Real-World Data (RWD) is the data relating to patient health status and/or the delivery of healthcare routinely collected from various sources.
Electronic Health Records (EHRs) is the Digital versions of patients’ paper charts, including comprehensive patient data such as medical history, diagnoses, medications, treatment plans, immunization dates, and test results.
The guidance focuses on the critical role of RWD in enhancing the FDA’s ability to evaluate the safety and efficacy of drug and biological products. It outlines the parameters for utilizing EHRs and medical claims data, ensuring that these data sources meet the necessary standards for accuracy, reliability, and relevance.
The FDA emphasizes the importance of selecting appropriate data sources and the key considerations include:
Relevance of the Data Source: Assessing whether the data source is suitable for the intended regulatory purpose.
Data Capture: Ensuring comprehensive and accurate data capture, including:
Enrollment and Comprehensive Capture of Care: Strategies to achieve complete and accurate patient data capture.
Data Linkage and Synthesis: Techniques for combining data from multiple sources to form a cohesive dataset.
Distributed Data Networks: Leveraging networks that aggregate data from various sources while maintaining patient privacy.
Computable Phenotypes: Developing precise definitions of clinical conditions and outcomes that can be consistently identified across datasets.
Unstructured Data: Handling and extracting meaningful information from unstructured data, such as free-text notes in EHRs.
Missing Data: Strategies for addressing and minimizing the impact of missing data in the dataset.
Validation: Ensuring the reliability of data through validation processes, including:
Conceptual and Operational Definitions of Study Variables: Establishing clear definitions for key study variables.
Validation Approaches: Techniques for confirming the accuracy and consistency of data.
Study Design Elements
This guidance covers essential aspects of study design when utilizing RWD, such as:
Definition of Time Periods: Determining the relevant time frames for data analysis.
Selection of Study Population: Criteria for including or excluding individuals from the study cohort.
Exposure Ascertainment and Validation: Methods for accurately identifying and validating exposure to the drug or biologic under study.
Outcome Ascertainment and Validation: Approaches for defining, identifying, and validating clinical outcomes of interest, including specific considerations for mortality as an outcome.
Covariate Ascertainment and Validation: Identification and validation of covariates, including confounders and effect modifiers, that may influence study outcomes.
Data Quality During Data Accrual, Curation, and Transformation
Ensuring high data quality is crucial throughout the data lifecycle. The guidance outlines best practices for:
Characterizing Data: Comprehensive documentation of the data’s characteristics, including source, completeness, and consistency.
Documentation of the QA/QC Plan: Maintaining detailed records of quality assurance and quality control processes.
Documentation of Data Management Process: Thorough documentation of the procedures followed during data curation and transformation.
For more comprehensive details, the full guidance document is available on the FDA’s website: Providing Over-the-Counter Monograph Submissions in Electronic Format.
Comments