Informative Censoring—A Cause of Bias in Estimating COVID-19 Mortality Using Hospital Data
Hung-Mo Lin,
Sean T. H. Liu,
Matthew A. Levin,
John Williamson,
Nicole M. Bouvier,
Judith A. Aberg,
David Reich,
Natalia Egorova
Affiliations
Hung-Mo Lin
Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
Sean T. H. Liu
Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
Matthew A. Levin
Department of Anesthesiology, Perioperative and Pain Medicine, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
John Williamson
Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
Nicole M. Bouvier
Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
Judith A. Aberg
Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
David Reich
Department of Anesthesiology, Perioperative and Pain Medicine, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
Natalia Egorova
Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
(1) Background: Several retrospective observational analyzed treatment outcomes for COVID-19; (2) Methods: Inverse probability of censoring weighting (IPCW) was applied to correct for bias due to informative censoring in database of hospitalized patients who did and did not receive convalescent plasma; (3) Results: When compared with an IPCW analysis, overall mortality was overestimated using an unadjusted Kaplan–Meier curve, and hazard ratios for the older age group compared to the youngest were underestimated using the Cox proportional hazard models and 30-day mortality; (4) Conclusions: An IPCW analysis provided stabilizing weights by hospital admission.
