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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Dades bibliogràfiques

Autor principal:	Casiraghi, Elena (author)
Altres autors:	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)
Format:	article
Idioma:	anglès
Publicat:	2023
Matèries:	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)
Accés en línia:	https://doi.org/10.1016/j.jbi.2023.104295 http://hdl.handle.net/20.500.14066/4735
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