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

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Sequentially testing for a gene-drug interaction in a genomewide analysis. / Kelly, Patrick; Zhou, Yinghui; Whitehead, John; Stallard, Nigel; Bowman, Clive.

In: Statistics in Medicine, Vol. 27, No. 11, 20.05.2008, p. 2022-2034.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Kelly, P, Zhou, Y, Whitehead, J, Stallard, N & Bowman, C 2008, 'Sequentially testing for a gene-drug interaction in a genomewide analysis.', Statistics in Medicine, vol. 27, no. 11, pp. 2022-2034. https://doi.org/10.1002/sim.3059

APA

Kelly, P., Zhou, Y., Whitehead, J., Stallard, N., & Bowman, C. (2008). Sequentially testing for a gene-drug interaction in a genomewide analysis. Statistics in Medicine, 27(11), 2022-2034. https://doi.org/10.1002/sim.3059

Vancouver

Kelly P, Zhou Y, Whitehead J, Stallard N, Bowman C. Sequentially testing for a gene-drug interaction in a genomewide analysis. Statistics in Medicine. 2008 May 20;27(11):2022-2034. https://doi.org/10.1002/sim.3059

Author

Kelly, Patrick ; Zhou, Yinghui ; Whitehead, John ; Stallard, Nigel ; Bowman, Clive. / Sequentially testing for a gene-drug interaction in a genomewide analysis. In: Statistics in Medicine. 2008 ; Vol. 27, No. 11. pp. 2022-2034.

Bibtex

@article{a58fb83eb5db4d2fa7ebc5dc06d506b4,
title = "Sequentially testing for a gene-drug interaction in a genomewide analysis.",
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.",
keywords = "pharmacogenetics • clinical trials • group sequential designs • adaptive designs • drug development",
author = "Patrick Kelly and Yinghui Zhou and John Whitehead and Nigel Stallard and Clive Bowman",
year = "2008",
month = may,
day = "20",
doi = "10.1002/sim.3059",
language = "English",
volume = "27",
pages = "2022--2034",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "11",

}

RIS

TY - JOUR

T1 - Sequentially testing for a gene-drug interaction in a genomewide analysis.

AU - Kelly, Patrick

AU - Zhou, Yinghui

AU - Whitehead, John

AU - Stallard, Nigel

AU - Bowman, Clive

PY - 2008/5/20

Y1 - 2008/5/20

N2 - 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.

AB - 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.

KW - pharmacogenetics • clinical trials • group sequential designs • adaptive designs • drug development

U2 - 10.1002/sim.3059

DO - 10.1002/sim.3059

M3 - Journal article

VL - 27

SP - 2022

EP - 2034

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 11

ER -