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Survival analysis arises in many fields of study such as medicine, biology, engineering, public health, epidemiology, and economics. This short course provides an introduction to Bayesian survival analysis. Several topics are addressed: parametric models, semiparametric models, proportional hazards models, cure rate models, model selection and comparison, and design and monitoring of clinical trials. Several additional topics are discussed; noninformative and informative prior specifications, computing posterior quantities of interest, Bayesian hypothesis testing, variable selection, model selection with nn models, model checking techniques using Bayesian diagnostic methods, and Markov chain Monte Carlo (MCMC) algorithms for sampling from the posterior and predictive distributions. Several examples using the software package BUGS will be illustrated. In addition, live demos of the BUGS software and data analysis using Bayesian survival models will be given using a LCD projector.
The course presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all essentially from the health sciences, including cancer, AIDS, and the environment. Overall, this course will be applied in nature and will focus on the applications of Bayesian methods for research problems arising in the medical sciences. Live demo real data examples will be given using the BUGS software.
The prerequisites include one course in statistical inference and Bayesian theory at the level of Casella and Berger (1990) and Box and Tiao (1992). The audience's background should include some familiarity with basic Bayesian models. Exposure to two or three conjugate models should provide enough background to motivate the examination of the types of models upon which this short course will focus. The audience should also have some exposure to survival analysis at the introductory level, as this will help motivate the goals of the many examples. Thus, this course would be most suitable for those who are second- or third-year graduate students in statistics or biostatistics, or other related fields.
The presenters of this course strongly recommend the purchase of the textbook, Bayesian Survival Analysis by Ibrahim, Chen and Sinha for participation in this course.
Text: Bayesian Survival Analysis Publisher: Springer-Verlag $80.00
Fees: M- $325 ($430 after July 18), NM- $415 ($520 after July 18), SM- $200 ($325 after July 18)
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= Applied Session,
= Theme Session,
= Presenter