The course focuses on Bayesian analyses using the PHREG, GENMOD, and MCMC procedures. The examples include logistic regression, Cox proportional hazards model, general linear mixed model, zero-inflated Poisson model, and data containing missing values. A Bayesian analysis of a crossover design and a meta-analysis are also shown.

The self-study e-learning includes:

• Annotatable course notes in PDF format.
• Virtual lab time to practice.

Learn how to

• Explain the concepts of Bayesian analysis.
• Illustrate Bayesian analyses in PROC GENMOD, PROC PHREG, and PROC MCMC.
• Incorporate prior distributions in a Bayesian analysis.
• Illustrate a Bayesian analysis approach to a meta-analysis.

Who should attend

Biostatisticians, epidemiologists, and social scientists who are interested in the Bayesian analysis approach

Formats available	Standard Duration (duration can vary, see event schedule for details)
Classroom:	2 days
Live Web Classroom:	4 half-day session(s)	System Requirements
e-Learning:	14 hours/180 day license

Before attending this course, you should:

• Be able to create SAS data sets and manipulate data. You can gain this experience from the SAS Programming 2: Data Manipulation Techniques course.
• Have completed a statistics course such as the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression or Statistics 2: ANOVA and Regression course.

This course addresses SAS/STAT software.

This course is offered on the Windows platform only using SAS/STAT 14.2.

Introduction to Bayesian Analysis

• Introduce the basic concepts of Bayesian analysis.
• Compute the diagnostic plots and diagnostic statistics for model assessment.
• Discuss the advantages and disadvantages of Bayesian analysis.
• Illustrate a Bayesian analysis in PROC GENMOD and PROC PHREG.

Fitting Models with the MCMC Procedure

• Show the essential statements in PROC MCMC.
• Show the supported distributions in PROC MCMC.
• Fit a logistic regression model in PROC MCMC.
• Fit a general linear mixed model in PROC MCMC.
• Fit a zero-inflated Poisson model in PROC MCMC.
• Incorporate missing values in PROC MCMC.

Bayesian Approaches to Clinical Trials

• Use prior distributions in a Bayesian analysis.
• Illustrate a Bayesian approach to clinical trials using PROC MCMC.
• Illustrate the Bayesian approach to meta-analysis.