Linked Life Data

17252534

Source:http://linkedlifedata.com/resource/pubmed/id/17252534

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2007-4-9
pubmed:abstractText
There are numerous alternatives to the so-called Bonferroni adjustment to control for familywise Type I error among multiple tests. Yet, for the most part, these approaches disregard the correlation among endpoints. This can prove to be a conservative hypothesis testing strategy if the null hypothesis is false. The James procedure was proposed to account for the correlation structure among multiple continuous endpoints. Here, a simulation study evaluates the statistical power of the Hochberg and James adjustment strategies relative to that of the Bonferroni approach when used for multiple correlated binary variables. The simulations demonstrate that relative to the Bonferroni approach, neither alternative sacrifices power. The Hochberg approach has more statistical power for rho<or=0.50; whereas the James procedure provides more statistical power with higher rho, the common correlation among the multiple outcomes. A study of gender differences in New York City homicides is used to illustrate the approaches.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
c 2007 John Wiley & Sons, Ltd.
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
26
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1712-23
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Statistical power of multiplicity adjustment strategies for correlated binary endpoints.
pubmed:affiliation
Department of Psychiatry, Weill Cornell Medical College, Box 140, 525 East 68th Street, New York, NY 10021, USA. acleon@med.cornell.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural