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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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$2,000 USD GSA  $1,552.44
Course Code STBA42
Duration 2 days
Available Formats Classroom, Virtual
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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.
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