Lancaster EPrints

Sequentially testing for a gene-drug interaction in a genomewide analysis.

Kelly, Patrick and Zhou, Yinghui and Whitehead, John and Stallard, Nigel and Bowman, Clive (2008) Sequentially testing for a gene-drug interaction in a genomewide analysis. Statistics in Medicine, 27 (11). pp. 2022-2034.

Full text not available from this repository.

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.

Item Type: Article
Journal or Publication Title: Statistics in Medicine
Uncontrolled Keywords: pharmacogenetics • clinical trials • group sequential designs • adaptive designs • drug development
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 26956
Deposited By: Mr Richard Ingham
Deposited On: 19 Aug 2009 10:26
Refereed?: Yes
Published?: Published
Last Modified: 09 Oct 2013 14:49
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/26956

Actions (login required)

View Item