The Library
Sequentially testing for a gene-drug interaction in a genomewide analysis
Tools
Kelly, Patrick, Zhou, Yinghui, Whitehead, John, Stallard, Nigel and Bowman, Clive (2008) Sequentially testing for a gene-drug interaction in a genomewide analysis. Statistics in Medicine, Vol.27 (No.11). pp. 2022-2034. doi:10.1002/sim.3059 ISSN 0277-6715.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: http://dx.doi.org/10.1002/sim.3059
Abstract
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. Copyright (c) 2007 John Wiley & Sons, Ltd.
Item Type: | Journal Article | ||||
---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics R Medicine > R Medicine (General) R Medicine > RM Therapeutics. Pharmacology |
||||
Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
||||
Library of Congress Subject Headings (LCSH): | Pharmacogenetics -- Statistical methods, Drug development -- Statistical methods, Clinical trials, Experimental design | ||||
Journal or Publication Title: | Statistics in Medicine | ||||
Publisher: | John Wiley & Sons Ltd. | ||||
ISSN: | 0277-6715 | ||||
Official Date: | 20 May 2008 | ||||
Dates: |
|
||||
Volume: | Vol.27 | ||||
Number: | No.11 | ||||
Number of Pages: | 13 | ||||
Page Range: | pp. 2022-2034 | ||||
DOI: | 10.1002/sim.3059 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Funder: | GlaxoSmithKline (GSK) | ||||
Grant number: | G3014 (GSK) |
Data sourced from Thomson Reuters' Web of Knowledge
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |