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