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IJE Advance Access originally published online on April 27, 2006
International Journal of Epidemiology 2006 35(3):777-778; doi:10.1093/ije/dyl081
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2006; all rights reserved.

Article

Response: Bayesian perspectives for epidemiological research

Sander Greenland1,2

1 Department of Epidemiology, University of California, Los Angeles 90095-1772, USA
2 Department of Statistics, University of California, Los Angeles 90095-1772, USA

E-mail: lesdomes@ucla.edu

The first 10% of the full text of this article appears below.

Dr Carpenter1 and I agree on the value of Bayesian perspectives and the inappropriateness of Neyman–Pearsonian testing for epidemiology. Unfortunately, he misrepresents several of my positions and misunderstands the import of data priors.

To prevent misuse of Bayesian methods, the meaning of priors must be made clear. Shockingly to me, Carpenter dismisses data priors with ‘We don't need to think of priors as expressing prior bets, and then turn our priors into pseudo data to include in a statistical analysis. The latter process is useful, but not always easy, and a prior does not always lead to unique prior data.’ This passage overlooks every advantage of data priors:

  1. We need not think . . . [Full Text of this Article]


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