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Statistical tests and identifiability conditions for pooling and analyzing multisite datasets
When sample sizes are small, the ability to identify weak (but scientifically interesting) associations between a set of predictors and a response may be enhanced by pooling existing datasets. However, variations in acquisition methods and the distribution of participants or observations between dat...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5816202/ https://www.ncbi.nlm.nih.gov/pubmed/29386387 http://dx.doi.org/10.1073/pnas.1719747115 |
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author | Zhou, Hao Henry Singh, Vikas Johnson, Sterling C. Wahba, Grace |
author_facet | Zhou, Hao Henry Singh, Vikas Johnson, Sterling C. Wahba, Grace |
author_sort | Zhou, Hao Henry |
collection | PubMed |
description | When sample sizes are small, the ability to identify weak (but scientifically interesting) associations between a set of predictors and a response may be enhanced by pooling existing datasets. However, variations in acquisition methods and the distribution of participants or observations between datasets, especially due to the distributional shifts in some predictors, may obfuscate real effects when datasets are combined. We present a rigorous statistical treatment of this problem and identify conditions where we can correct the distributional shift. We also provide an algorithm for the situation where the correction is identifiable. We analyze various properties of the framework for testing model fit, constructing confidence intervals, and evaluating consistency characteristics. Our technical development is motivated by Alzheimer’s disease (AD) studies, and we present empirical results showing that our framework enables harmonizing of protein biomarkers, even when the assays across sites differ. Our contribution may, in part, mitigate a bottleneck that researchers face in clinical research when pooling smaller sized datasets and may offer benefits when the subjects of interest are difficult to recruit or when resources prohibit large single-site studies. |
format | Online Article Text |
id | pubmed-5816202 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-58162022018-02-21 Statistical tests and identifiability conditions for pooling and analyzing multisite datasets Zhou, Hao Henry Singh, Vikas Johnson, Sterling C. Wahba, Grace Proc Natl Acad Sci U S A Physical Sciences When sample sizes are small, the ability to identify weak (but scientifically interesting) associations between a set of predictors and a response may be enhanced by pooling existing datasets. However, variations in acquisition methods and the distribution of participants or observations between datasets, especially due to the distributional shifts in some predictors, may obfuscate real effects when datasets are combined. We present a rigorous statistical treatment of this problem and identify conditions where we can correct the distributional shift. We also provide an algorithm for the situation where the correction is identifiable. We analyze various properties of the framework for testing model fit, constructing confidence intervals, and evaluating consistency characteristics. Our technical development is motivated by Alzheimer’s disease (AD) studies, and we present empirical results showing that our framework enables harmonizing of protein biomarkers, even when the assays across sites differ. Our contribution may, in part, mitigate a bottleneck that researchers face in clinical research when pooling smaller sized datasets and may offer benefits when the subjects of interest are difficult to recruit or when resources prohibit large single-site studies. National Academy of Sciences 2018-02-13 2018-01-31 /pmc/articles/PMC5816202/ /pubmed/29386387 http://dx.doi.org/10.1073/pnas.1719747115 Text en Copyright © 2018 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Physical Sciences Zhou, Hao Henry Singh, Vikas Johnson, Sterling C. Wahba, Grace Statistical tests and identifiability conditions for pooling and analyzing multisite datasets |
title | Statistical tests and identifiability conditions for pooling and analyzing multisite datasets |
title_full | Statistical tests and identifiability conditions for pooling and analyzing multisite datasets |
title_fullStr | Statistical tests and identifiability conditions for pooling and analyzing multisite datasets |
title_full_unstemmed | Statistical tests and identifiability conditions for pooling and analyzing multisite datasets |
title_short | Statistical tests and identifiability conditions for pooling and analyzing multisite datasets |
title_sort | statistical tests and identifiability conditions for pooling and analyzing multisite datasets |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5816202/ https://www.ncbi.nlm.nih.gov/pubmed/29386387 http://dx.doi.org/10.1073/pnas.1719747115 |
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