SDTM to ADaM Data Transformation

This project involves transforming Study Data Tabulation Model (SDTM) lab data into an Analysis Data Model (ADaM) compliant dataset, specifically for LB (Laboratory Data) data.

Process Overview

1. Read in the SDTM lab data: Load the SDTM LB dataset into the SAS workspace.

2. Check dataset structure: Use PROC CONTENTS to examine the structure of the SDTM LB dataset.

3. Create the ADaM LB (ADLB) dataset: Generate an ADLB dataset from the SDTM LB data.

4. Variable retention and derivation: Retain necessary variables for analysis and create derived variables for baseline and change from baseline.

5. Flag significant changes: Create a flag for significant changes from baseline.

6. Apply ADaM metadata attributes: This is a placeholder - actual metadata would be study-specific and would be applied based on the ADaM Implementation Guide.

7. Quality Control: Check SAS log for errors or warnings, and validate outputs. Ensure documentation for each step to maintain inspection readiness.

Clinical Trial Data Import and Analysis

1. Set the input data location: Specify the path to the clinical trial data.

2. Import the data into a SAS dataset: Load the CSV data into a SAS dataset using PROC IMPORT.

3. Check dataset structure: Use PROC CONTENTS to examine the structure of the imported data.

4. Data cleaning and manipulation: Recode categorical variables, handle missing values, and create derived variables.

5. Automate repetitive tasks: Create a macro to automate tasks like generating summary statistics for a list of variables.

6. Perform logistic regression: Use PROC LOGISTIC to perform a logistic regression analysis.

7. Quality Control: Check SAS log for errors or warnings, and validate outputs. Ensure documentation for each step to maintain inspection readiness.

Prerequisites

This project requires a solid understanding of SAS programming, SDTM and ADaM data standards, as well as familiarity with clinical trial data.

Contributing

Contributors are expected to adhere to ADaM Implementation Guide and relevant regulatory guidelines. Please ensure thorough documentation for each step for reproducibility and inspection readiness.

Author

Ojong Tabi

License

This project is licensed under the MIT License.

Acknowledgments

This work contributes to improving data transformation practices in clinical trial data analysis, and supports increased transparency and reproducibility.