Permutation-based group sequential analyses for cognitive neuroscience Article Swipe
John P. Veillette
,
Letitia Ho
,
Howard C. Nusbaum
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.02.27.530244
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.02.27.530244
Cognitive neuroscientists have been grappling with two related experimental design problems. First, the complexity of neuroimaging data (e.g. often hundreds of thousands of correlated measurements) and analysis pipelines demands bespoke, non-parametric statistical tests for valid inference, and these tests often lack an agreed-upon method for performing a priori power analyses. Thus, sample size determination for neuroimaging studies is often arbitrary or inferred from other putatively but questionably similar studies, which can result in underpowered designs – undermining the efficacy of neuroimaging research. Second, when meta-analyses estimate the sample sizes required to obtain reasonable statistical power, estimated sample sizes can be prohibitively large given the resource constraints of many labs. We propose the use of sequential analyses to partially address both of these problems. Sequential study designs – in which the data is analyzed at interim points during data collection and data collection can be stopped if the planned test statistic satisfies a stopping rule specified a priori – are common in the clinical trial literature, due to the efficiency gains they afford over fixed-sample designs. However, the corrections used to control false positive rates in existing approaches to sequential testing rely on parametric assumptions that are often violated in neuroimaging settings. We introduce a general permutation scheme that allows sequential designs to be used with arbitrary test statistics. By simulation, we show that this scheme controls the false positive rate across multiple interim analyses. Then, performing power analyses for seven evoked response effects seen in the EEG literature, we show that this sequential analysis approach can substantially outperform fixed-sample approaches (i.e. require fewer subjects, on average, to detect a true effect) when study designs are sufficiently well-powered. To facilitate the adoption of this methodology, we provide a Python package “niseq” with sequential implementations of common tests used for neuroimaging: cluster-based permutation tests, threshold-free cluster enhancement, t -max, F -max, and the network-based statistic with tutorial examples using EEG and fMRI data.
Related Topics
Concepts
Sample size determination
Resampling
Computer science
False discovery rate
Permutation (music)
Neuroimaging
Test statistic
Statistical power
Interim
Multiple comparisons problem
Statistical hypothesis testing
Sequential analysis
A priori and a posteriori
Interim analysis
Sample (material)
Inference
Type I and type II errors
Parametric statistics
Data mining
Artificial intelligence
Statistics
Psychology
Mathematics
Clinical trial
Medicine
Chromatography
Psychiatry
Acoustics
Epistemology
Biochemistry
Physics
Philosophy
Gene
History
Pathology
Chemistry
Archaeology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.02.27.530244
- https://www.biorxiv.org/content/biorxiv/early/2023/02/28/2023.02.27.530244.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4322628444
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4322628444Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2023.02.27.530244Digital Object Identifier
- Title
-
Permutation-based group sequential analyses for cognitive neuroscienceWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-28Full publication date if available
- Authors
-
John P. Veillette, Letitia Ho, Howard C. NusbaumList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.02.27.530244Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/02/28/2023.02.27.530244.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/02/28/2023.02.27.530244.full.pdfDirect OA link when available
- Concepts
-
Sample size determination, Resampling, Computer science, False discovery rate, Permutation (music), Neuroimaging, Test statistic, Statistical power, Interim, Multiple comparisons problem, Statistical hypothesis testing, Sequential analysis, A priori and a posteriori, Interim analysis, Sample (material), Inference, Type I and type II errors, Parametric statistics, Data mining, Artificial intelligence, Statistics, Psychology, Mathematics, Clinical trial, Medicine, Chromatography, Psychiatry, Acoustics, Epistemology, Biochemistry, Physics, Philosophy, Gene, History, Pathology, Chemistry, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
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33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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