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Bayesian Analyses Using SAS(R)

Course code: STBA42

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.
1 200 EUR

1 452 EUR including VAT

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 21 hours

Language: en

Price without VAT: 1 380 EUR

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Starting date: Upon request

Type: Upon request

Course duration: 14 hours

Language: en

Price without VAT: 1 200 EUR

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Starting
date		 Place
Type Course
duration		 Language Price without VAT
Upon request E-learning 21 hours en 1 380 EUR Register
Upon request Upon request 14 hours en 1 200 EUR Register
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Target group

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

Course structure

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.

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 Programmierung 2: Datenmanagement 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.

    • Do you need advice or a tailor-made course?

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