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.

Ismerje meg hogyan...

• 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.

Kinek ajánljuk

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

Elérhető képzési formák	Időtartam
Tantermi:	2.0 nap
Live Web tanfolyam:	4 fél napos

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 the Statistics 2: ANOVA and Regression courses.

A tanfolyam SAS/STAT szoftver használatára épül.

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