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Understanding CDISC-like Data Structures
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CDISC stands for Clinical Data Interchange Standards Consortium. It is an organization that develops global data standards to support the collection, management, and submission of clinical trial data. These standards help ensure that clinical data is consistent, structured, and easily understood by regulatory agencies such as the FDA and EMA.
CDISC standards are widely used in the pharmaceutical industry to organize and submit clinical trial data. They provide a common structure that allows researchers, sponsors, and regulators to interpret data in a consistent way.
Two of the most commonly used CDISC data models are SDTM and ADaM.
| Model | Full Name | Purpose |
|---|---|---|
| SDTM | Study Data Tabulation Model | Standard format for raw clinical trial data submission |
| ADaM | Analysis Data Model | Dataset structure used for statistical analysis |
The SDTM model organizes clinical trial data into specific domains, where each domain represents a type of information collected during the trial.
| Domain | Description |
|---|---|
| DM | Demographics data for each subject |
| AE | Adverse events experienced by subjects |
| LB | Laboratory test results |
| VS | Vital signs such as blood pressure and heart rate |
| EX | Exposure to the study drug |
The ADaM model is derived from SDTM datasets and is specifically designed for statistical analysis. It contains analysis-ready datasets that include derived variables, flags, and population indicators used in statistical testing.
In R, CDISC-like data structures are often represented as data frames where each domain is stored as a separate dataset.
# Example SDTM-like Demographics dataset
DM <- data.frame(
USUBJID = c("SUBJ001", "SUBJ002", "SUBJ003"),
AGE = c(45, 52, 37),
SEX = c("M", "F", "M"),
TRTGRP = c("Drug", "Placebo", "Drug")
)
# View dataset
str(DM)
summary(DM)Understanding CDISC-like data structures is important for working with clinical trial data because it ensures compliance with regulatory requirements and supports accurate, reproducible analysis. These standardized formats make it easier to share, review, and interpret clinical data across different organizations.
