Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://www.medrxiv.org/content/10.1101/2023.07.27.23293272v1, first published .
Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study

Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study

Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study

Zachary Butzin-Dozier   1 , PhD ;   Yunwen Ji   1 , MA ;   Haodong Li   1 , MA ;   Jeremy Coyle   1 , PhD ;   Junming Shi   1 , MA ;   Rachael V Phillips   1 , PhD ;   Andrew N Mertens   1 , PhD ;   Romain Pirracchio   2 , MD ;   Mark J van der Laan   1 , PhD ;   Rena C Patel   3 , MD ;   John M Colford   1 , MD, PhD ;   Alan E Hubbard   1 , PhD ;   The National COVID Cohort Collaborative (N3C) Consortium   4

1 Division of Biostatistics, University of California Berkeley School of Public Health, Berkeley, CA, United States

2 Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, United States

3 Department of Infectious Diseases, University of Alabama at Birmingham School of Medicine, Birmingham, AL, United States

4 Members are listed at the end of the manuscript, Rockville, MD, United States

Corresponding Author:

  • Zachary Butzin-Dozier, PhD
  • Division of Biostatistics
  • University of California Berkeley School of Public Health
  • 2121 Berkeley Way
  • Berkeley, CA, 94720
  • United States
  • Phone: 1 3024376262
  • Email: zbutzin@berkeley.edu