Some places remain for Bayesian Biostatistics at MUSC.
A course sequence of 2 workshops will be presented during the MUSC
Summer Institute on Research Methodology. The sequence will consist of
an Introductory Bayesian Biostatistics course (IBB), and an Advance
Bayesian Biostatistics course (ABB). The IBB course is designed to
provide a basic grounding in Bayesian modeling methods and hands-on
experience with WinBUGS, INLA and demonstration of SAS capabilities. The
ABB course will cover specific application areas in more depth and is
designed to be a continuation of the IBB course.
Bayesian Biostatistics: an Introduction (May 2nd - 3rd)
Topics covered
* What is Bayesian Biostatistics;
* Bayesian Basics: likelihood, priors, posterior
* Hierarchical models; DAGS; Posterior sampling; MCMC;
* Examples comparing conventional and Bayesian analyses will be covered
* Demo of basic examples in WinBUGS and SAS
* Hands – on with R and WinBUGS
* Random effect models: LMM and GLMM
* Other software: R2WinBUGS, OpenBUGS, BRugs, JAGS, INLA
Bayesian Biostatistics: Advanced (May 5th – 6th)
Topics covered
* Parametric survival modeling and cox regression
* Longitudinal modeling
* Measurement error
* Handling missing data
- Forms of missingness and solutions
- Joint models for drop-out
* Special topics:
- Imaging/disease mapping
- Variable selection
- INLA examples
Presenters:
Professor Andrew B. Lawson
Professor Mulugeta Gebregziabher
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This is one of the workshops in the Summer Institute held at MUSC in May
2016
For more details of the courses available please refer to the following:
Registration:
Course fee: The IBB course fee includes the text, Lesaffre, E. and
Lawson, A. B. (2012) Bayesian Biostatistics, Wiley , New York from which
many examples are taken. The fee is $600.
The fee for the ABB course is $600. If both courses are attended then
the fee is discounted to $1000.
Registration forms and brochure are available at:
http://academicdepartments.musc.edu/phs/docs/SummerInstitute_2016.pdf
Email enquiries to June Watson: [log in to unmask]
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