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
MDPI
Subjects
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
United States
United States > US
New York City New York
COVID-19
censoring
convalescent plasma
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Summary:(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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2075-1729
2075-1729
DOI:10.3390/life13010210