About the course
Welcome to our comprehensive Clinical SAS Programming course, designed to equip you with the skills to excel in biostatistics and data analysis within the clinical research industry. This program focuses on using SAS for data manipulation, statistical analysis, and report generation, adhering to industry standards and regulatory requirements. Learn to effectively manage and analyze clinical trial data, contributing to the development of new treatments and therapies.
Course content
- SAS Fundamentals:
- Introduction to the SAS environment and programming language.
- Data types, variables, and operators.
- Clinical Data Management Overview:
- Understanding the role of SAS in clinical trials.
- Data flow and lifecycle in clinical research.
- CDISC Standards:
- Introduction to CDISC (SDTM and ADaM) standards.
- Importance of data standardization in regulatory submissions.
- Data Input and Output:
- Importing and exporting data from various sources (CSV, Excel, databases).
- Creating and managing SAS datasets.
- Data Manipulation Techniques:
- Data cleaning and validation.
- Merging, sorting, and subsetting datasets.
- Using SAS functions and procedures.
- Macro Programming:
- Introduction to SAS macros.
- Creating reusable code and automating tasks.
- Descriptive Statistics:
- Calculating summary statistics and generating tables.
- Using PROC MEANS, PROC FREQ, and PROC UNIVARIATE.
- Inferential Statistics:
- Hypothesis testing and confidence intervals.
- Using PROC TTEST, PROC ANOVA, and PROC CORR.
- Survival Analysis:
- Kaplan-Meier survival curves and Cox proportional hazards models.
- Using PROC LIFETEST and PROC PHREG.
- Categorical Data Analysis:
- Chi-square tests and logistic regression.
- Using PROC LOGISTIC and PROC CATMOD.
- Table, Listing, and Graph (TLG) Programming:
- Generating TLGs for clinical study reports.
- Using PROC REPORT, PROC TABULATE, and PROC GPLOT.
- Creating Patient Profiles:
- Developing patient-level data summaries.
- Using SAS/GRAPH for data visualization.
- Adverse Event Reporting:
- Generating reports for adverse events and serious adverse events.
- Understanding MedDRA coding in SAS.
- Creating ADaM Datasets:
- Developing ADaM datasets for statistical analysis.
- Understanding ADaM specifications.
- Validation and Quality Control:
- Validating SAS programs and outputs.
- Ensuring compliance with regulatory guidelines.
- Creating Define.XML files.
- SAS Optimization and Performance Tuning:
- Optimizing SAS code for efficiency.
- Debugging and troubleshooting SAS programs.
- Using SAS ODS (Output Delivery System):
- Generating reports in various formats (PDF, RTF, HTML).
- Customizing report layouts and styles.
- Industry Best Practices and Standards:
- Adhering to coding standards and guidelines.
- Documentation and version control.
- Real-World Clinical Trial Datasets:
- Analyzing and reporting on clinical trial data.
- Applying learned concepts to practical scenarios.
- Project Work:
- Developing a SAS program for a specific clinical trial analysis.
- Presenting and documenting the analysis results.
