Clinical trials are research studies conducted in humans to evaluate the safety and effectiveness of medical treatments, interventions, or devices. These trials are essential steps in the process of developing new treatments or improving existing ones.
In clinical trials, data transformation refers to the process of preparing and organizing clinical trial data according to specific CDISC standards. CDISC standards are used to streamline the collection, management, and submission of clinical trial data to regulatory authorities such as the FDA (Food and Drug Administration), EMA (European Medicines Agency), etc. Here’s how data transformation is applied within CDISC standards:
CDISC Standards and Data Transformation:
SDTM (Study Data Tabulation Model):
Data Mapping: SDTM defines a standardized structure for organizing data collected during a clinical trial. Data transformation involves mapping raw data elements (from various sources (such as EDC, Third Party data etc.) and formats) to SDTM domains, variables, and controlled terminology.
Variable Transformation: Raw data may need to be transformed to fit the specific format and definitions required by SDTM domains (e.g., converting dates to SDTM date format, coding categorical data using CDISC terminology).
ADaM (Analysis Data Model):
Dataset Creation: ADaM provides a standard structure for analysis datasets used in statistical analyses. Data transformation involves creating ADaM datasets from SDTM data, which may include aggregating data, deriving new variables, and applying statistical methods to prepare data for analysis.
Variable Derivation: Variables needed for statistical analysis are derived from SDTM data in accordance with ADaM specifications.
CDASH (Clinical Data Acquisition Standards Harmonization):
Data Collection: CDASH provides standards for the collection of clinical trial data in electronic case report forms (eCRFs). While CDASH primarily focuses on data collection rather than transformation, adherence to CDASH standards ensures that data collected during the trial are structured and formatted in a way that facilitates subsequent data transformation into SDTM.
Processes Involved in Data Transformation in CDISC:
Data Standardization: Transforming diverse and heterogeneous raw data from clinical trials into a standardized format (SDTM) ensures consistency and interoperability.
Controlled Terminology: Applying CDISC-controlled terminology ensures that variables are coded uniformly across trials, facilitating data analysis and regulatory submissions.
Metadata Creation: Metadata describing the transformation processes and decisions made (e.g., mappings, derivations) are documented to ensure traceability and transparency.
Benefits of CDISC Data Transformation:
Interoperability: CDISC standards enable interoperability between different clinical trial datasets and systems, facilitating data exchange and integration.
Standardization: Ensures consistency in data structure and format across clinical trials, facilitating data integration and comparison.
Efficiency: By standardizing data transformation processes, CDISC reduces the time and effort required for data management and regulatory submissions.
Regulatory Compliance: Submission of clinical trial data in CDISC-compliant formats supports regulatory requirements and speeds up the regulatory review process.
In conclusion, data transformation in CDISC involves converting raw clinical trial data into standardized formats (SDTM, ADaM) using defined processes and controlled terminology. This transformation ensures data consistency, interoperability, and compliance with regulatory standards, thereby facilitating efficient data management and analysis in clinical research.
By Amal Anandan
Principal Data Scientist Innovative Solutions & Strategist
