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IJE Advance Access originally published online on May 3, 2008
International Journal of Epidemiology 2008 37(3):430-434; doi:10.1093/ije/dyn064
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2008; all rights reserved.

Editorials

Multiple comparisons and association selection in general epidemiology

Sander Greenland

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

E-mail: lesdomes@ucla.edu

Accepted 12 March 2008

The first 150 words of the full text of this article appear below.

In this issue of the journal, Prof. Jon Wakefield provides a contribution to the complex topic of screening genetic associations.1 My comments here are intended to clarify some points and outline connections of his discussion to broader problems, describing how methods such as Wakefield's can be appropriate in epidemiology beyond genetic research. I will also comment on a few aspects of his presentation related to technical issues. I will assume (as does Wakefield) that the reader is familiar with the terminology of Bayesian statistics2 as well that of conventional (frequentist) statistics.

I will start with a comment on interpretation of P-values, and then turn to the issue of multiple comparisons.

Proper interpretation of P-values and confidence intervals

As Wakefield mentions, for continuous distributions a valid frequentist P-value is uniformly distributed under the null hypothesis. From this fact, we can deduce that, under the null and assuming no bias, 0.3% of the P-values will be . . . [Full Text of this Article]

Multiple-comparisons problems are real—and common

Defensible methods for association selection

P-values = bad Bayes factors

Conclusion

Appendix


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