A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

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Title: A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.
Authors: Casiraghi E; AnacletoLab, Department of Computer Science 'Giovanni degli Antoni', Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA., Wong R; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA., Hall M; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA., Coleman B; The Jackson Laboratory for Genomic Medicine, Farmington, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA., Notaro M; AnacletoLab, Department of Computer Science 'Giovanni degli Antoni', Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy., Evans MD; Biostatistical Design and Analysis Center, Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USA., Tronieri JS; Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA., Blau H; The Jackson Laboratory for Genomic Medicine, Farmington, USA., Laraway B; University of Colorado, Anschutz Medical Campus, Aurora, CO, USA., Callahan TJ; University of Colorado, Anschutz Medical Campus, Aurora, CO, USA., Chan LE; College of Public Health and Human Sciences, Oregon State University, Corvallis, USA., Bramante CT; Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA., Buse JB; NC Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Endocrinology, Department of Medicine, University of North Carolina School of Medicine, USA., Moffitt RA; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA., Stürmer T; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Johnson SG; Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA., Raymond Shao Y; Harvard-MIT Division of Health Sciences and Technology (HST), 260 Longwood Ave, Boston, USA; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, USA., Reese J; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA., Robinson PN; The Jackson Laboratory for Genomic Medicine, Farmington, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA., Paccanaro A; School of Applied Mathematics (EMAp), Fundação Getúlio Vargas, Rio de Janeiro, Brazil; Department of Computer Science, Royal Holloway, University of London, Egham, UK., Valentini G; AnacletoLab, Department of Computer Science 'Giovanni degli Antoni', Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy., Huling JD; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA., Wilkins KJ; Biostatistics Program, Office of the Director, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
Corporate Authors: N3C Consortium
Source: Journal of biomedical informatics [J Biomed Inform] 2023 Mar; Vol. 139, pp. 104295. Date of Electronic Publication: 2023 Jan 27.
Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, N.I.H., Extramural
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 100970413 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-0480 (Electronic) Linking ISSN: 15320464 NLM ISO Abbreviation: J Biomed Inform Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1532-0480
DOI:10.1016/j.jbi.2023.104295