Linked Life Data

17979181

Source:http://linkedlifedata.com/resource/pubmed/id/17979181

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
2008-4-22
pubmed:abstractText
Assaying a large number of genetic markers from patients in clinical trials is now possible in order to tailor drugs with respect to efficacy. The statistical methodology for analysing such massive data sets is challenging. The most popular type of statistical analysis is to use a univariate test for each genetic marker, once all the data from a clinical study have been collected. This paper presents a sequential method for conducting an omnibus test for detecting gene-drug interactions across the genome, thus allowing informed decisions at the earliest opportunity and overcoming the multiple testing problems from conducting many univariate tests. We first propose an omnibus test for a fixed sample size. This test is based on combining F-statistics that test for an interaction between treatment and the individual single nucleotide polymorphism (SNP). As SNPs tend to be correlated, we use permutations to calculate a global p-value. We extend our omnibus test to the sequential case. In order to control the type I error rate, we propose a sequential method that uses permutations to obtain the stopping boundaries. The results of a simulation study show that the sequential permutation method is more powerful than alternative sequential methods that control the type I error rate, such as the inverse-normal method. The proposed method is flexible as we do not need to assume a mode of inheritance and can also adjust for confounding factors. An application to real clinical data illustrates that the method is computationally feasible for a large number of SNPs.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2007 John Wiley & Sons, Ltd.
pubmed:issnType
Print
pubmed:day
20
pubmed:volume
27
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2022-34
pubmed:dateRevised
2009-11-3
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Sequentially testing for a gene-drug interaction in a genomewide analysis.
pubmed:affiliation
School of Public Health, The University of Sydney, Sydney, NSW 2006, Australia. pkelly@health.usyd.edu.au
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't