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. 2023 Jan 11;13(1):210.
doi: 10.3390/life13010210.

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

Hung-Mo Lin  1 , Sean T H Liu  2 , Matthew A Levin  3 , John Williamson  4 , Nicole M Bouvier  2 , Judith A Aberg  2 , David Reich  3 , Natalia Egorova  1
Affiliations

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

Hung-Mo Lin et al. Life (Basel). .

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.

Keywords: COVID-19; censoring; convalescent plasma.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic diagram for the inverse probability of censoring weighting analysis.
Figure 2
Figure 2
Comparisons of different survival curves after index admission for COVID-19 for patients who received convalescent plasma therapy in Mount Sinai Health System.
See this image and copyright information in PMC

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