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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number210
JournalLife
Volume13
Issue number1
DOIs
StatePublished - Jan 2023

Keywords

  • COVID-19
  • censoring
  • convalescent plasma

Fingerprint

Dive into the research topics of 'Informative Censoring—A Cause of Bias in Estimating COVID-19 Mortality Using Hospital Data'. Together they form a unique fingerprint.

Cite this