When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Bayesian Analyses Using SAS(R)
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,...
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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.
Skills Gained
- 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 Can Benefit
- Biostatisticians, epidemiologists, and social scientists who are interested in the Bayesian analysis approach
Prerequisites
- 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.
Course Details
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.