Breaking the circularity in circular analyses: simulations and formal treatment of the flattened average approach

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Breaking the circularity in circular analyses : simulations and formal treatment of the flattened average approach. / Bowman, Howard; Brooks, Joseph; Hajilou, Omid; Zoumpoulaki, Alexia; Litvak, Vladimir.

In: PLoS Computational Biology, Vol. 16, No. 11, e1008286, 23.11.2020.

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Bowman, Howard ; Brooks, Joseph ; Hajilou, Omid ; Zoumpoulaki, Alexia ; Litvak, Vladimir. / Breaking the circularity in circular analyses : simulations and formal treatment of the flattened average approach. In: PLoS Computational Biology. 2020 ; Vol. 16, No. 11.

Bibtex

@article{678e751bf0bc4c7e832c577bedc5b809,
title = "Breaking the circularity in circular analyses: simulations and formal treatment of the flattened average approach",
abstract = "There has been considerable debate and concern as to whether there is a replication crisis in the scientific literature. A likely cause of poor replication is the multiple comparisons problem. An important way in which this problem can manifest in the M/EEG context is through post hoc tailoring of analysis windows (a.k.a. regions-of-interest, ROIs) to landmarks in the collected data. Post hoc tailoring of ROIs is used because it allows researchers to adapt to inter-experiment variability and discover novel differences that fall outside of windows defined by prior precedent, thereby reducing Type II errors. However, this approach can dramatically inflate Type I error rates. One way to avoid this problem is to tailor windows according to a contrast that is orthogonal (strictly parametrically orthogonal) to the contrast being tested. A key approach of this kind is to identify windows on a fully flattened average. On the basis of simulations, this approach has been argued to be safe for post hoc tailoring of analysis windows under many conditions. Here, we present further simulations and mathematical proofs to show exactly why the Fully Flattened Average approach is unbiased, providing a formal grounding to the approach, clarifying the limits of its applicability and resolving published misconceptions about the method. We also provide a statistical power analysis, which shows that, in specific contexts, the fully flattened average approach provides higher statistical power than Fieldtrip cluster inference. This suggests that the Fully Flattened Average approach will enable researchers to identify more effects from their data without incurring an inflation of the false positive rate.",
keywords = "neuroimaging analysis, orthogonal contrast, double dipping, region of interest, EEG",
author = "Howard Bowman and Joseph Brooks and Omid Hajilou and Alexia Zoumpoulaki and Vladimir Litvak",
year = "2020",
month = nov,
day = "23",
doi = "10.1371/journal.pcbi.1008286",
language = "English",
volume = "16",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science (PLOS)",
number = "11",

}

RIS

TY - JOUR

T1 - Breaking the circularity in circular analyses

T2 - simulations and formal treatment of the flattened average approach

AU - Bowman, Howard

AU - Brooks, Joseph

AU - Hajilou, Omid

AU - Zoumpoulaki, Alexia

AU - Litvak, Vladimir

PY - 2020/11/23

Y1 - 2020/11/23

N2 - There has been considerable debate and concern as to whether there is a replication crisis in the scientific literature. A likely cause of poor replication is the multiple comparisons problem. An important way in which this problem can manifest in the M/EEG context is through post hoc tailoring of analysis windows (a.k.a. regions-of-interest, ROIs) to landmarks in the collected data. Post hoc tailoring of ROIs is used because it allows researchers to adapt to inter-experiment variability and discover novel differences that fall outside of windows defined by prior precedent, thereby reducing Type II errors. However, this approach can dramatically inflate Type I error rates. One way to avoid this problem is to tailor windows according to a contrast that is orthogonal (strictly parametrically orthogonal) to the contrast being tested. A key approach of this kind is to identify windows on a fully flattened average. On the basis of simulations, this approach has been argued to be safe for post hoc tailoring of analysis windows under many conditions. Here, we present further simulations and mathematical proofs to show exactly why the Fully Flattened Average approach is unbiased, providing a formal grounding to the approach, clarifying the limits of its applicability and resolving published misconceptions about the method. We also provide a statistical power analysis, which shows that, in specific contexts, the fully flattened average approach provides higher statistical power than Fieldtrip cluster inference. This suggests that the Fully Flattened Average approach will enable researchers to identify more effects from their data without incurring an inflation of the false positive rate.

AB - There has been considerable debate and concern as to whether there is a replication crisis in the scientific literature. A likely cause of poor replication is the multiple comparisons problem. An important way in which this problem can manifest in the M/EEG context is through post hoc tailoring of analysis windows (a.k.a. regions-of-interest, ROIs) to landmarks in the collected data. Post hoc tailoring of ROIs is used because it allows researchers to adapt to inter-experiment variability and discover novel differences that fall outside of windows defined by prior precedent, thereby reducing Type II errors. However, this approach can dramatically inflate Type I error rates. One way to avoid this problem is to tailor windows according to a contrast that is orthogonal (strictly parametrically orthogonal) to the contrast being tested. A key approach of this kind is to identify windows on a fully flattened average. On the basis of simulations, this approach has been argued to be safe for post hoc tailoring of analysis windows under many conditions. Here, we present further simulations and mathematical proofs to show exactly why the Fully Flattened Average approach is unbiased, providing a formal grounding to the approach, clarifying the limits of its applicability and resolving published misconceptions about the method. We also provide a statistical power analysis, which shows that, in specific contexts, the fully flattened average approach provides higher statistical power than Fieldtrip cluster inference. This suggests that the Fully Flattened Average approach will enable researchers to identify more effects from their data without incurring an inflation of the false positive rate.

KW - neuroimaging analysis

KW - orthogonal contrast

KW - double dipping

KW - region of interest

KW - EEG

U2 - 10.1371/journal.pcbi.1008286

DO - 10.1371/journal.pcbi.1008286

M3 - Article

VL - 16

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 11

M1 - e1008286

ER -