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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Hlavní autor:	Casiraghi, Elena (author)
Další autoři:	Wong, Rachel (author), Hall, Margaret (author), Coleman, Ben (author), Notaro, Marco (author), Evans, Michael D. (author), Tronieri, Jena S. (author), Blau, Hannah (author), Laraway, Bryan (author), Callahan, Tiffany J. (author), Chan, Lauren E. (author), Bramante, Carolyn T. (author), Buse, John B. (author), Moffitt, Richard A. (author), Stürmer, Til (author), Johnson, Steven G. (author), Shao, Yu Raymond (author), Reese, Justin (author), Robinson, Peter N. (author), Paccanaro, Alberto (author), Valentini, Giorgio (author), Huling, Jared D. (author), Wilkins, Kenneth J. (author), N3C Consortium (author)
Médium:	article
Jazyk:	angličtina
Vydáno:	2023
Témata:	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)
On-line přístup:	https://doi.org/10.1016/j.jbi.2023.104295 http://hdl.handle.net/20.500.14066/4735
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