Topics Covered in clinical SAS
In clinical trials, data collection, validation, and management are all important.
Compliance with regulatory regulations (for example, FDA and EMA).
CDISC (Clinical Data Interchange Standards Consortium) standards, such as SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model), must be understood and implemented.
Importing and exporting clinical trial data into SAS datasets from multiple sources such as Excel, databases, and other formats.
Data transformation and modification are used to prepare datasets for statistical analysis.
SAS programming is used for data cleansing and standardisation.
Validation tests and data cleaning techniques are used to ensure data quality.
Detecting and resolving data errors and conflicts.
Performing statistical studies such as descriptive statistics, inferential statistics, and modelling (for example, regression and survival analysis).
Creating clinical study tables, lists, and figures (TLFs).
Analysing safety (adverse events) and effectiveness (primary and secondary outcomes) data.
Results interpretation and reporting to assist regulatory filings.
For standardised reporting, raw data is mapped to SDTM domains.
Creating and producing ADaM datasets for analysis.
SAS programming is used to create analytical datasets, summarise statistics, and generate reports.
For efficiency, specialised macros and reusable code are created.
Ensuring adherence to regulatory rules and standards (for example, ICH-GCP, 21 CFR Part 11).
Data preparation and submission for regulatory assessments and clearances.
Clinical study reports (CSRs) and other regulatory documentation are created.
Making tables, figures, and lists (TFLs) for regulatory filings.