Hypothesis testing, power and sample size determination for between group comparisons in fMRI experiments

Dulal K. Bhaumik; Anindya Roy; Nicole A. Lazar; Kush Kapur; Subhash Aryal; John A. Sweeney; Dave Patterson; Robert D. Gibbons

doi:10.1016/j.stamet.2008.05.003

Hypothesis testing, power and sample size determination for between group comparisons in fMRI experiments

Dulal K. Bhaumik, Anindya Roy, Nicole A. Lazar, Kush Kapur, Subhash Aryal, John A. Sweeney, Dave Patterson, Robert D. Gibbons

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

Modern methods for imaging the human brain, such as functional magnetic resonance imaging (fMRI) present a range of challenging statistical problems. In this paper, we first develop a large sample based test for between group comparisons and use it to determine the necessary sample size in order to obtain a target power via simulation under various alternatives for a given pre-specified significance level. Both testing and sample size calculations are particularly critical for neuroscientists who use these new techniques, since each subject is expensive to image.

Original language	English (US)
Pages (from-to)	133-146
Number of pages	14
Journal	Statistical Methodology
Volume	6
Issue number	2
DOIs	https://doi.org/10.1016/j.stamet.2008.05.003
State	Published - Mar 2009

All Science Journal Classification (ASJC) codes

Statistics and Probability

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10.1016/j.stamet.2008.05.003

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abstract = "Modern methods for imaging the human brain, such as functional magnetic resonance imaging (fMRI) present a range of challenging statistical problems. In this paper, we first develop a large sample based test for between group comparisons and use it to determine the necessary sample size in order to obtain a target power via simulation under various alternatives for a given pre-specified significance level. Both testing and sample size calculations are particularly critical for neuroscientists who use these new techniques, since each subject is expensive to image.",

author = "Bhaumik, \{Dulal K.\} and Anindya Roy and Lazar, \{Nicole A.\} and Kush Kapur and Subhash Aryal and Sweeney, \{John A.\} and Dave Patterson and Gibbons, \{Robert D.\}",

note = "Funding Information: The authors thank an associate editor and two referees for their insightful comments that significantly improved the quality of this article. This work was supported in part by a grant from the National Institute of Mental Health (1P50HD055751-01).",

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AU - Bhaumik, Dulal K.

AU - Roy, Anindya

AU - Lazar, Nicole A.

AU - Kapur, Kush

AU - Aryal, Subhash

AU - Sweeney, John A.

AU - Patterson, Dave

AU - Gibbons, Robert D.

N1 - Funding Information: The authors thank an associate editor and two referees for their insightful comments that significantly improved the quality of this article. This work was supported in part by a grant from the National Institute of Mental Health (1P50HD055751-01).

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AB - Modern methods for imaging the human brain, such as functional magnetic resonance imaging (fMRI) present a range of challenging statistical problems. In this paper, we first develop a large sample based test for between group comparisons and use it to determine the necessary sample size in order to obtain a target power via simulation under various alternatives for a given pre-specified significance level. Both testing and sample size calculations are particularly critical for neuroscientists who use these new techniques, since each subject is expensive to image.

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Hypothesis testing, power and sample size determination for between group comparisons in fMRI experiments

Abstract

All Science Journal Classification (ASJC) codes

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