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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Bibliografiske detaljer

Hovedforfatter:	Casiraghi, Elena (author)
Andre forfattere:	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
Sprog:	engelsk
Udgivet:	2023
Fag:	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)
Online adgang:	https://doi.org/10.1016/j.jbi.2023.104295 http://hdl.handle.net/20.500.14066/4735
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