A method for comparing multiple imputation techniques : a case study on the U.S. national COVID cohort collaborative
Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe...
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| Kolejni autorzy: | , , , , , , , , , , , , , , , , , , , , , , |
| Format: | article |
| Język: | angielski |
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2023
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| Hasła przedmiotowe: | |
| Dostęp online: | https://doi.org/10.1016/j.jbi.2023.104295 http://hdl.handle.net/20.500.14066/4735 |
| Etykiety: |
Nie ma etykietki, Dołącz pierwszą etykiete!
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| _version_ | 1870612070178750464 |
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| author | Casiraghi, Elena |
| author2 | Wong, Rachel Hall, Margaret Coleman, Ben Notaro, Marco Evans, Michael D. Tronieri, Jena S. Blau, Hannah Laraway, Bryan Callahan, Tiffany J. Chan, Lauren E. Bramante, Carolyn T. Buse, John B. Moffitt, Richard A. Stürmer, Til Johnson, Steven G. Shao, Yu Raymond Reese, Justin Robinson, Peter N. Paccanaro, Alberto Valentini, Giorgio Huling, Jared D. Wilkins, Kenneth J. N3C Consortium |
| author2_role | author author author author author author author author author author author author author author author author author author author author author author author |
| author_browse | Blau, Hannah Bramante, Carolyn T. Buse, John B. Callahan, Tiffany J. Casiraghi, Elena Chan, Lauren E. Coleman, Ben Evans, Michael D. Hall, Margaret Huling, Jared D. Johnson, Steven G. Laraway, Bryan Moffitt, Richard A. N3C Consortium Notaro, Marco Paccanaro, Alberto Reese, Justin Robinson, Peter N. Shao, Yu Raymond Stürmer, Til Tronieri, Jena S. Valentini, Giorgio Wilkins, Kenneth J. Wong, Rachel |
| author_facet | Casiraghi, Elena Wong, Rachel Hall, Margaret Coleman, Ben Notaro, Marco Evans, Michael D. Tronieri, Jena S. Blau, Hannah Laraway, Bryan Callahan, Tiffany J. Chan, Lauren E. Bramante, Carolyn T. Buse, John B. Moffitt, Richard A. Stürmer, Til Johnson, Steven G. Shao, Yu Raymond Reese, Justin Robinson, Peter N. Paccanaro, Alberto Valentini, Giorgio Huling, Jared D. Wilkins, Kenneth J. N3C Consortium |
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| dc.contributor.other.es.fl_str_mv | Universidad Católica Nuestra Señora de la Asunción Cámara Paraguaya de Exportadores y Comercializadores de Cereales y Oleaginosas Centro de Ingeniería para la Investigación, Desarrollo e Innovación Tecnológica |
| dc.creator.none.fl_str_mv | Casiraghi, Elena Wong, Rachel Hall, Margaret Coleman, Ben Notaro, Marco Evans, Michael D. Tronieri, Jena S. Blau, Hannah Laraway, Bryan Callahan, Tiffany J. Chan, Lauren E. Bramante, Carolyn T. Buse, John B. Moffitt, Richard A. Stürmer, Til Johnson, Steven G. Shao, Yu Raymond Reese, Justin Robinson, Peter N. Paccanaro, Alberto Valentini, Giorgio Huling, Jared D. Wilkins, Kenneth J. N3C Consortium |
| dc.date.accessioned.none.fl_str_mv | 2026-01-06T18:57:34Z |
| dc.date.available.none.fl_str_mv | 2026-01-06T18:57:34Z |
| dc.date.issued.none.fl_str_mv | 2023-01-27 |
| dc.format.extent.es.fl_str_mv | 28 páginas |
| dc.identifier.citation.en.fl_str_mv | Casiraghi, E., Wong, R., Hall, M., Coleman, B., Notaro, M., Evans, M. D., Tronieri, J. S., Blau, H., Laraway, B., Callahan, T., Chan, L. E., Bramante, C. T., Buse, J. B., Moffitt, R. A., Stürmer, T., Johnson, S. G., Shao, Y. R., Reese, J., Robinson, P. N., … Wilkins, K. J. (2023). A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative. Journal of Biomedical Informatics, 139, Artículo 104295. https://doi.org/10.1016/j.jbi.2023.104295 |
| dc.identifier.doi.es.fl_str_mv | 10.1016/j.jbi.2023.104295 |
| dc.identifier.essn.es.fl_str_mv | 1532-0480 |
| dc.identifier.issn.es.fl_str_mv | 1532-0464 |
| dc.identifier.other.es.fl_str_mv | https://doi.org/10.1016/j.jbi.2023.104295 |
| dc.identifier.uri.none.fl_str_mv | http://hdl.handle.net/20.500.14066/4735 |
| dc.language.iso.es.fl_str_mv | eng |
| dc.publisher.es.fl_str_mv | Elsevier |
| dc.relation.projectCONACYT.es.fl_str_mv | 14-INV-088 PINV15-315 PINV20-337 |
| dc.rights.*.fl_str_mv | Atribución/Reconocimiento-NoComercial-SinDerivados 4.0 Internacional |
| dc.rights.accessRights.es.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.rights.uri.*.fl_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.subject.classification.en.fl_str_mv | 6.16. Manufacture of basic pharmaceutical products and pharmaceutical preparations |
| dc.subject.classification.es.fl_str_mv | 6. Producción y tecnología industrial 8. Agricultura 8.2. Fertilizantes químicos, biocidas, control biológico de plagas y mecanización de la agricultura |
| dc.subject.ocde.es.fl_str_mv | 4. Ciencias Agrícolas y Veterinarias 4.1. Agricultura, silvicultura, pesca y ciencias afines (agronomía, zootecnia, pesca, silvicultura, horticultura, otras disciplinas afines) |
| dc.subject.other.es.fl_str_mv | Clinical informatics COVID-19 severity assessment Diabetic patients Evaluation framework Multiple Imputation |
| dc.title.es.fl_str_mv | A method for comparing multiple imputation techniques : a case study on the U.S. national COVID cohort collaborative |
| dc.type.es.fl_str_mv | info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm’s parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | CONACYT_9a03a9e77586be4efec6b024f403e6fa |
| identifier_str_mv | Casiraghi, E., Wong, R., Hall, M., Coleman, B., Notaro, M., Evans, M. D., Tronieri, J. S., Blau, H., Laraway, B., Callahan, T., Chan, L. E., Bramante, C. T., Buse, J. B., Moffitt, R. A., Stürmer, T., Johnson, S. G., Shao, Y. R., Reese, J., Robinson, P. N., … Wilkins, K. J. (2023). A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative. Journal of Biomedical Informatics, 139, Artículo 104295. https://doi.org/10.1016/j.jbi.2023.104295 1532-0464 10.1016/j.jbi.2023.104295 1532-0480 |
| language | eng |
| network_acronym_str | CONACYT |
| network_name_str | Repositorio Institucional CONACYT |
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| publishDate | 2023 |
| publishDateSort | 2023 |
| repository.mail.fl_str_mv | repositorio.institucional@conacyt.gov.py |
| repository.name.fl_str_mv | Repositorio Institucional CONACYT |
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| rights_invalid_str_mv | Atribución/Reconocimiento-NoComercial-SinDerivados 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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D., Tronieri, J. S., Blau, H., Laraway, B., Callahan, T., Chan, L. E., Bramante, C. T., Buse, J. B., Moffitt, R. A., Stürmer, T., Johnson, S. G., Shao, Y. R., Reese, J., Robinson, P. N., … Wilkins, K. J. (2023). A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative. Journal of Biomedical Informatics, 139, Artículo 104295. https://doi.org/10.1016/j.jbi.2023.1042951532-0464https://doi.org/10.1016/j.jbi.2023.104295http://hdl.handle.net/20.500.14066/473510.1016/j.jbi.2023.1042951532-0480Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm’s parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.Consejo Nacional de Ciencia y TecnologíaPrograma Paraguayo para el Desarrollo de la Ciencia y Tecnología. Proyectos de investigación y desarrollo28 páginasengElsevierAtribución/Reconocimiento-NoComercial-SinDerivados 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccess© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync- nd/4.0/).6. Producción y tecnología industrial8. Agricultura8.2. Fertilizantes químicos, biocidas, control biológico de plagas y mecanización de la agricultura6.16. Manufacture of basic pharmaceutical products and pharmaceutical preparationsClinical informaticsCOVID-19 severity assessmentDiabetic patientsEvaluation frameworkMultiple Imputation4. Ciencias Agrícolas y Veterinarias4.1. Agricultura, silvicultura, pesca y ciencias afines (agronomía, zootecnia, pesca, silvicultura, horticultura, otras disciplinas afines)A method for comparing multiple imputation techniques : a case study on the U.S. national COVID cohort collaborativeinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionJournal of Biomedical Informatics14-INV-088PINV15-315PINV20-337139Casiraghi, ElenaWong, RachelHall, MargaretColeman, BenNotaro, MarcoEvans, Michael D.Tronieri, Jena S.Blau, HannahLaraway, BryanCallahan, Tiffany J.Chan, Lauren E.Bramante, Carolyn T.Buse, John B.Moffitt, Richard A.Stürmer, TilJohnson, Steven G.Shao, Yu RaymondReese, JustinRobinson, Peter N.Paccanaro, AlbertoValentini, GiorgioHuling, Jared D.Wilkins, Kenneth J.N3C ConsortiumCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4735/1/license_rdf4460e5956bc1d1639be9ae6146a50347MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81698http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4735/2/license.txt858b22fda432bd774e469302988c1974MD52ORIGINALA method for comparing multiple imputation techniques: a case study on the U.S. national COVID cohort collaborative.pdfA method for comparing multiple imputation techniques: a case study on the U.S. national COVID cohort collaborative.pdfArtículo científicoapplication/pdf9453089http://repositorio.conacyt.gov.py/bitstream/20.500.14066/4735/3/A%20method%20for%20comparing%20multiple%20imputation%20techniques%3a%20a%20case%20study%20on%20the%20U.S.%20national%20COVID%20cohort%20collaborative.pdfb6e723933542168abd5ec0461b5ae7bfMD53Appendix B. 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| spellingShingle | A method for comparing multiple imputation techniques : a case study on the U.S. national COVID cohort collaborative Casiraghi, Elena 6. Producción y tecnología industrial 8. Agricultura 8.2. Fertilizantes químicos, biocidas, control biológico de plagas y mecanización de la agricultura 6.16. Manufacture of basic pharmaceutical products and pharmaceutical preparations Clinical informatics COVID-19 severity assessment Diabetic patients Evaluation framework Multiple Imputation 4. Ciencias Agrícolas y Veterinarias 4.1. Agricultura, silvicultura, pesca y ciencias afines (agronomía, zootecnia, pesca, silvicultura, horticultura, otras disciplinas afines) |
| status_str | publishedVersion |
| title | A method for comparing multiple imputation techniques : a case study on the U.S. national COVID cohort collaborative |
| title_full | A method for comparing multiple imputation techniques : a case study on the U.S. national COVID cohort collaborative |
| title_fullStr | A method for comparing multiple imputation techniques : a case study on the U.S. national COVID cohort collaborative |
| title_full_unstemmed | A method for comparing multiple imputation techniques : a case study on the U.S. national COVID cohort collaborative |
| title_short | A method for comparing multiple imputation techniques : a case study on the U.S. national COVID cohort collaborative |
| title_sort | A method for comparing multiple imputation techniques : a case study on the U.S. national COVID cohort collaborative |
| topic | 6. Producción y tecnología industrial 8. Agricultura 8.2. Fertilizantes químicos, biocidas, control biológico de plagas y mecanización de la agricultura 6.16. Manufacture of basic pharmaceutical products and pharmaceutical preparations Clinical informatics COVID-19 severity assessment Diabetic patients Evaluation framework Multiple Imputation 4. Ciencias Agrícolas y Veterinarias 4.1. Agricultura, silvicultura, pesca y ciencias afines (agronomía, zootecnia, pesca, silvicultura, horticultura, otras disciplinas afines) |
| url | https://doi.org/10.1016/j.jbi.2023.104295 http://hdl.handle.net/20.500.14066/4735 |