WHO Collaborating Centre for Reference and Research on Influenza (VIDRL)
Peter Doherty Institute for Infection and Immunity
792 Elizabeth Street
Melbourne VIC 3000 Australia
T +61 3 9342 9300
F +61 3 9342 9329
Bayesian and Penalised Regression Methods for Epidemiological Analysis
PLEASE NOTE THAT THIS COURSE HAS ALREADY BEEN COMPLETED.
The Centre hosted this short course for statisticians and epidemiologists in July 2014.
Presenter: Sander Greenland
Professor Sander Greenland is one of the most prolific and influential authors on epidemiological methods of the past 2-3 decades, including a co-author (with K Rothman) of the widely prescribed textbook ‘Modern Epidemiology’ and a first author of 177 articles in epidemiology and biostatistics journals.
Background: Bayesian methods continue to become more popular in statistical modelling, but are not covered in most basic teaching. This lag may in part be due to common misconceptions (encouraged by most expositions) that Bayesian methods are conceptually distinct from frequentist methods and require special software. In fact, Bayesian methods are examples of penalized ("shrinkage") estimation and thus are perfectly acceptable frequentist methods; conversely, common frequentist methods are special types of Bayesian methods in which prior distributions are noninformative (so penalties are either zero or infinite). This short course will explain and illustrate the relationship between the two perspectives with real examples and will show how penalization allows one to deal with a number of common problems that render ordinary statistical methods misleading for epidemiological research.
For further information about this course please contact Sheena Sullivan by email: email@example.com or fax: (03) 9342 9329
Greenland, S. (2006). Bayesian perspectives for epidemiologic research. I. Foundations and basic methods. Int J Epidemiol, 35: 765-778. comment and reply. Related Stata file
Greenland, S. (2007). Bayesian perspectives for epidemiologic research. II. Regression analysis. Int J Epidemiol, 36: 195-202. Related Stata file
Greenland, S. (2009). Bayesian perspectives for epidemiologic research. III. Bias analysis via missing-data methods. Int J Epidemiol, 38: 1662- 1673. Corrigenda
Cole, S., Chu, H., and Greenland, S. (2014) Maximum likelihood, profile likelihood, and penalized likelihood: a primer. Am J Epidemiol, 179: 252-60. Related SAS file
Greenland, S. (2007). Prior data for non-normal priors. Statist Med, 2007, 26: 3578–90
Greenland, S. (2008). Invited Commentary: Variable Selection versus Shrinkage in the Control of Multiple Confounders. Am J Epidemiol. 167: 523-529.
ADO files for Stata users (You will need to right click and save these files to the directory C:\ado\plus\p) pllf.zip