Clinical SAS Course Content

  1. Overview

    SAS Clinical Data Integration provides fast, efficient access to clinical, operational and safety data, regardless of location or source. This enables you to improve your time to market while containing clinical research costs. The software automates the migration of acquired data assets through data standards, while supporting and automating data aggregation and standardization for ongoing clinical trials. Faster data preparation also facilitates meeting the requirements of medical publications.

  2. Clinical SAS Course Outline

    Clinical Trials Process
    • Describe the clinical research process (phases, key roles, key organizations).
    • Interpret a Statistical Analysis Plan.
    • Derive programming requirements from an SAP and an annotated Case Report Form.
    • Describe regulatory requirements (principles of 21 CFR Part 11, International Conference on Harmonization, Good Clinical Practices).

    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.

    Import and Export Clinical Trials Data
    • Combine SAS data sets.
    • Use list input to read raw data files (csv or tab delimited files).
    • Efficiently import and subset SAS data sets.
    • Access data in an Excel workbook (LIBNAME and PROC IMPORT/EXPORT).
    • Create temporary and permanent SAS data sets.
    • Apply regulatory requirements to exported SAS data sets (RAS V5 requirements).

    Manage Clinical Trials Data
    • Investigate SAS data libraries using base SAS utility procedures (PRINT, CONTENTS, FREQ).
    • Access Dictionary Tables using the SQL procedure.
    • Sort observations in a SAS data set.
    • Create and modify variable attributes using options and statements in the DATA step.
    • Examine and explore clinical trials input data (find outliers, missing vs. zero values, etc).

    Transform Clinical Trials Data
    • Process data using DO LOOPS.
    • Process data using SAS arrays.
    • Retain variables across observations.
    • Use assignment statements in the DATA step.
    • Apply categorization and windowing techniques to clinical trials data.
    • Use SAS functions to convert character data to numeric and vice versa.
    • Use SAS functions to manipulate character data, numeric data, and SAS date values.
    • Transpose SAS data sets.
    • Apply ‘observation carry forward techniques’ to clinical trials data (LOCF, BOCF, WOCF).
    • Calculate ‘change from baseline’ results.
    • Obtain counts of events in clinical trials.

    Apply Statistical Procedures for Clinical Trials
    • Use SAS procedures to obtain descriptive statistics for clinical trials data (FREQ, UNIVARIATE, MEANS, SUMMARY).
    • Use PROC FREQ to obtain p-values for categorical data (2×2 and NxP test for association).
    • Use PROC TTEST to obtain p-values for continuous data (one-sample, paired and two-sample t-tests).
    • Identify and describe SAS procedures used to perform ANOVA.
    • Create output data sets from statistical procedures.

    Macro Programming for Clinical Trials
    • Create and use user-defined and automatic macro variables.
    • Automate programs by defining and calling macros.
    • Use system options to debug macros and display values of macro variables in the SAS log (MPRINT, SYMBOLGEN, MLOGIC, MACROGEN).

    Report Clinical Trials Results
    • Use PROC REPORT to produce tables and listings for clinical trials reports.
    • Use ODS and global statements to produce and augment clinical trials reports.
    • Use PROC GPLOT to create graphs for clinical trials reports.
    • USD ODS Statistical Graphing to create graphs for clinical trials reports.

    Validate Clinical Trial Data Reporting
    • Explain the principles of programming validation in the clinical trial industry.
    • Utilize the log file to validate clinical trial data reporting.
    • Use programming techniques to validate clinical trial data reporting (PROC COMPARE, MSGLEVEL).
    • Identify and Resolve data, syntax and logic errors.

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