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Analyzing nested experimental designs—A user-friendly resampling method to determine experimental significance

While hierarchical experimental designs are near-ubiquitous in neuroscience and biomedical research, researchers often do not take the structure of their datasets into account while performing statistical hypothesis tests. Resampling-based methods are a flexible strategy for performing these analyse...

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Detalles Bibliográficos
Autores principales: Kulkarni, Rishikesh U., Wang, Catherine L., Bertozzi, Carolyn R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9098003/
https://www.ncbi.nlm.nih.gov/pubmed/35500032
http://dx.doi.org/10.1371/journal.pcbi.1010061
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author Kulkarni, Rishikesh U.
Wang, Catherine L.
Bertozzi, Carolyn R.
author_facet Kulkarni, Rishikesh U.
Wang, Catherine L.
Bertozzi, Carolyn R.
author_sort Kulkarni, Rishikesh U.
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description While hierarchical experimental designs are near-ubiquitous in neuroscience and biomedical research, researchers often do not take the structure of their datasets into account while performing statistical hypothesis tests. Resampling-based methods are a flexible strategy for performing these analyses but are difficult due to the lack of open-source software to automate test construction and execution. To address this, we present Hierarch, a Python package to perform hypothesis tests and compute confidence intervals on hierarchical experimental designs. Using a combination of permutation resampling and bootstrap aggregation, Hierarch can be used to perform hypothesis tests that maintain nominal Type I error rates and generate confidence intervals that maintain the nominal coverage probability without making distributional assumptions about the dataset of interest. Hierarch makes use of the Numba JIT compiler to reduce p-value computation times to under one second for typical datasets in biomedical research. Hierarch also enables researchers to construct user-defined resampling plans that take advantage of Hierarch’s Numba-accelerated functions.
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spelling pubmed-90980032022-05-13 Analyzing nested experimental designs—A user-friendly resampling method to determine experimental significance Kulkarni, Rishikesh U. Wang, Catherine L. Bertozzi, Carolyn R. PLoS Comput Biol Research Article While hierarchical experimental designs are near-ubiquitous in neuroscience and biomedical research, researchers often do not take the structure of their datasets into account while performing statistical hypothesis tests. Resampling-based methods are a flexible strategy for performing these analyses but are difficult due to the lack of open-source software to automate test construction and execution. To address this, we present Hierarch, a Python package to perform hypothesis tests and compute confidence intervals on hierarchical experimental designs. Using a combination of permutation resampling and bootstrap aggregation, Hierarch can be used to perform hypothesis tests that maintain nominal Type I error rates and generate confidence intervals that maintain the nominal coverage probability without making distributional assumptions about the dataset of interest. Hierarch makes use of the Numba JIT compiler to reduce p-value computation times to under one second for typical datasets in biomedical research. Hierarch also enables researchers to construct user-defined resampling plans that take advantage of Hierarch’s Numba-accelerated functions. Public Library of Science 2022-05-02 /pmc/articles/PMC9098003/ /pubmed/35500032 http://dx.doi.org/10.1371/journal.pcbi.1010061 Text en © 2022 Kulkarni et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kulkarni, Rishikesh U.
Wang, Catherine L.
Bertozzi, Carolyn R.
Analyzing nested experimental designs—A user-friendly resampling method to determine experimental significance
title Analyzing nested experimental designs—A user-friendly resampling method to determine experimental significance
title_full Analyzing nested experimental designs—A user-friendly resampling method to determine experimental significance
title_fullStr Analyzing nested experimental designs—A user-friendly resampling method to determine experimental significance
title_full_unstemmed Analyzing nested experimental designs—A user-friendly resampling method to determine experimental significance
title_short Analyzing nested experimental designs—A user-friendly resampling method to determine experimental significance
title_sort analyzing nested experimental designs—a user-friendly resampling method to determine experimental significance
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9098003/
https://www.ncbi.nlm.nih.gov/pubmed/35500032
http://dx.doi.org/10.1371/journal.pcbi.1010061
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