SAS Clinical Training

SAS Clinical training provides insight on the practical aspects of clinical trials. How to design, analyze and generate reports are explained in support of real time data. The skills of writing SAS programs, importing/exporting data and transforming it further for performing analysis on it is imparted by the industry experts. The training will equip the candidates with theoretical knowledge, practical experience, detailed description of programs, and key techniques involved in the clinical trial analysis.

By the end of SAS Clinical training you will exhibit the following capabilities:

• Illustrate the fundamental knowledge of trial designs, alternative trial designs and statistical analysis
• Access, manage, and transform clinical trials data
• Create tables, listings, and clinical trial graphs
• Use PROC REQ and PROC UNIVARIATE to export descriptive statistics
• Work on the analysis of stratified data
• Interpret various methods used for multiple comparisons and multiple endpoints
• Decide reference intervals for safety and diagnostic measures
• Evaluate results from incomplete data

Target audience

• Life Science or Bioinformatics graduates
• SAS programmer
• Clinical Programmer

Prerequisites

• Knowledge of Base SAS
• Deep understanding of clinical programming concepts
• Basic understanding of statistics

Module I: Clinical Trials a Practical Guide to Design, Analysis, and Reporting

• Randomized Clinical Trials
• Uncontrolled Trials
• Protocol Development
• Endpoints
• Patient Selection
• Source and Control of Bias
• Randomization
• Blinding
• Sample Size and Power

• Crossover Trials
• Factorial Design
• Equivalence Trials
• Bioequivalence Trials
• Noninferiority Trials
• Cluster Randomized Trials
• Multicenter Trials

• Types of Data and Normal Distribution
• Significance Tests and Confidence Intervals
• Comparison of Means
• Comparison of Proportions
• Analysis of Survival Data  235

• Intention-to-Treat Analysis
• Subgroup Analysis
• Regression Analysis
• Adjustment for Covariates
• Confounding
• Interaction
• Repeated Measurements
• Multiplicity
• Missing Data
• Interim Monitoring and Stopping Rules

• Overview of Reporting
• Trial Profile
• Presenting Baseline Data
• Use of Tables
• Use of Figures
• Critical Appraisal of a Report
• Meta-Analysis

Module II: SAS® Programming in the Pharmaceutical Industry

• Preparing and Classifying Clinical
• Importing Data
• Transforming Data and Creating Analysis
• Creating Tables and Listings
• Creating Tables
• General Approach to Creating Tables
• Using PROC TABULATE to Create Clinical Trial Tables
• Using PROC REPORT to Create Clinical Trial Tables
• Creating Continuous/Categorical Summary Tables
• Creating Adverse Event Summaries
• Creating Concomitant or Prior Medication Tables
• Creating a Laboratory Shift Table

• Common Clinical Trial Graphs
• Scatter Plot
• Line Plot
• Bar Chart
• Box Plot
• Odds Ratio Plot
• Kaplan-Meier Survival Estimates Plot

• Statistics
• Obtaining Descriptive Statistics
• Using PROC FREQ to Export Descriptive Statistics
• Using PROC UNIVARIATE to Export Descriptive Statistics
• Obtaining Inferential Statistics from Categorical Data Analysis
• Performing a 2x2 Test for Association
• Performing an NxP Test for Association
• Performing a Stratified NxP Test for Association
• Performing Logistic Regression
• Obtaining Inferential Statistics from Continuous Data Analysis
• Performing a One-Sample Test of the Mean
• Performing a Two-Sample Test of the Means
• Performing an N-Sample Test of the Means
• Obtaining Time-to-Event Analysis Statistics
• Obtaining Correlation Coefficients
• General Approach to Obtaining Statistics

• Changes in the Business Environment
• Changes in Technology
• Changes in Regulations
• Changes in Standards
• Use of SAS Software in the Clinical Trial Industry

Module III: Analysis of Clinical Trials Using SAS

• Introduction
• Continuous Endpoints
• Categorical Endpoints
• Time-to-Event Endpoints
• Tests for Qualitative Interactions

• Introduction
• Single-Step Tests
• Closed Testing Methods
• Fixed-Sequence Testing Methods
• Resampling-Based Testing Methods
• Testing Procedures for Multiple Endpoints
• Gatekeeping Strategies

• Introduction
• Reference Intervals for Safety and Diagnostic Measures
• Analysis of Shift Tables

• Introduction
• Repeated Significance Tests
• Stochastic Curtailment Tests
• Analysis of Incomplete Data
• Introduction
• Case Studies
• Data Setting and Modeling Framework
• Analysis of Complete Growth Data
• Simple Methods and MCAR
• Available Case Methods
• Likelihood-Based Ignorable Analyses
• Multiple Imputations
• The EM Algorithm
• Categorical Data
• MNAR and Sensitivity Analysis