Clinical SAS Course Syllabus

Getting Started With SAS

• Basic overview about SAS software
• Basic about programming

Working With SAS Syntax

• Fundamental concepts
• Characteristics of SAS statements
• Explain SAS syntax rules

Getting Familiar With SAS Dataset

• Descriptor & Data portions
• Accessing SAS libraries

Reading SAS Datasets

• How to read data in SAS
• SAS data as input
• Observations & variables
• Descriptor & Data portions

Reading Excel Worksheets

• Overview about importing Excel file

Reading Delimited Raw Data Files

• How to read raw data
• Compilation & Execution phases of Data step

Validating And Cleaning Data

• Procedures for validating data
• Techniques for cleaning data

Manipulating Data

• Variable creation
• Sub setting Observation

Combining SAS Datasets

• Appending, concatenating dataset and Merging the SAS dataset

Enhancing Report (ODS Systems)

• Global statements
• Format & Label Statement and User Defined formats
• Sending output to external files (HTML,PDF,RTF)

Summary Reports

• FREQ Procedure
• MEANS Procedure
• How to use procedures in Clinical trials

Controlling Input And Output

• Outputting multiple Observation
• Writing to Multiple SAS Datasets
• Selecting Variables & Observations

Reading Raw Data Files Data Transformations

• Manipulating character & numeric variables
• Converting Variable type

Processing Data Iteratively

• Do loop processing
• SAS Arrays

Introduction of SAS and Clinical research

• SAS role in Clinical Research.
• What is Clinical trial?
• What is Protocol and role of Protocol in Clinical Research?
• Which is playing main role in Clinical Research?

Clinical Trials Data Structures

• Identify the classes of clinical trials data (demographic, lab, baseline, concomitant medication, etc.).
• Identify key CDISC principals and terms.
• Describe the structure and purpose of the CDISC SDTM data model.
• Describe the structure and purpose of the CDISC ADaM data model.
• Describe the contents and purpose of define.xml.

Transform Clinical Trials Data

• Apply categorization and windowing techniques to clinical trials data.
• Transpose SAS data sets.
• Apply ‘observation carry forward’ techniques to clinical trials data (LOCF, BOCF, WOCF).
• Calculate ‘change from baseline’ results.