Informative Censoring-A Cause of Bias in Estimating COVID-19 Mortality Using Hospital Data

(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 plas...

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Published inLife (Basel, Switzerland) Vol. 13; no. 1; p. 210
Main Authors Lin, Hung-Mo, Liu, Sean T H, Levin, Matthew A, Williamson, John, Bouvier, Nicole M, Aberg, Judith A, Reich, David, Egorova, Natalia
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 01.01.2023
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Abstract (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.
AbstractList (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.
Audience Academic
Author Lin, Hung-Mo
Williamson, John
Liu, Sean T H
Bouvier, Nicole M
Aberg, Judith A
Egorova, Natalia
Reich, David
Levin, Matthew A
AuthorAffiliation 1 Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
3 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
2 Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
4 Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
AuthorAffiliation_xml – name: 3 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
– name: 4 Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
– name: 2 Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
– name: 1 Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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Issue 1
Keywords COVID-19
censoring
convalescent plasma
Language English
License Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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StartPage 210
SubjectTerms Analysis
Bias
Bias (Statistics)
censoring
Censoring (Statistics)
convalescent plasma
Coronaviruses
COVID-19
Electronic data processing
Hospital care
Medical research
Medicine, Experimental
Methods
Mortality
Patient outcomes
Statistical models
Statistics
United States
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Title Informative Censoring-A Cause of Bias in Estimating COVID-19 Mortality Using Hospital Data
URI https://www.ncbi.nlm.nih.gov/pubmed/36676159
https://www.proquest.com/docview/2767237464/abstract/
https://search.proquest.com/docview/2768223915
https://pubmed.ncbi.nlm.nih.gov/PMC9865049
https://doaj.org/article/9ab274f5164e47fa9720a54a8cd3c3fd
Volume 13
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