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Extending the p-plot: Heuristics for multiple testing

Published online by Cambridge University Press:  01 September 1999

IAN ABRAMSON ,
TANYA WOLFSON ,
THOMAS D. MARCOTTE ,
IGOR GRANT  and
THE HNRC GROUP
IAN ABRAMSON
Affiliation:
Department of Mathematics, University of California at San Diego
TANYA WOLFSON
Affiliation:
Department of Psychiatry, University of California at San Diego
THOMAS D. MARCOTTE
Affiliation:
Department of Psychiatry, University of California at San Diego
IGOR GRANT
Affiliation:
Department of Psychiatry, University of California at San Diego Psychiatry Service, VA San Diego Healthcare System
THE HNRC GROUP
Affiliation:
Department of Psychiatry, University of California at San Diego
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Abstract

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In the problem of large-scale multiple testing the p-plot is a graphically based competitor to the notoriously weak Bonferroni method. The p-plot is less stringent and more revealing in that it gives a gauge of how many hypotheses are decidedly false. The method is elucidated and extended here: the bootstrap reveals bias and sampling error in the usual point estimates, a bootstrap-based confidence interval for the gauge is given, as well as two acceptably powerful blanket tests of significance. Guidelines for use are given, and interpretational pitfalls pointed out, in the discussion of a case study linking premortem neuropsychological and postmortem neuropathologic data in an HIV cohort study. (JINS, 1999, 5, 510–517)

Keywords

Multiple testingP-plotBonferroniBootstrap
Type
Research Article
Information
Journal of the International Neuropsychological Society , Volume 5 , Issue 6 , September 1999 , pp. 510 - 517
Copyright
© 1999 The International Neuropsychological Society