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Informative Censoring-A Cause of Bias in Estimating COVID-19 Mortality Using Hospital Data.
Hung-Mo Lin1, Sean T H Liu2, Matthew A Levin3
1Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Life (Basel, Switzerland)
|January 21, 2023
Summary
Inverse probability of censoring weighting (IPCW) improved COVID-19 treatment outcome analysis by correcting for informative censoring. This method provided more accurate mortality and hazard ratio estimates compared to traditional analyses.
Area of Science:
- Epidemiology
- Biostatistics
- Infectious Diseases
Background:
- Retrospective observational studies are crucial for analyzing COVID-19 treatment outcomes.
- Convalescent plasma therapy has been investigated as a potential treatment for hospitalized COVID-19 patients.
- Informative censoring can introduce bias in survival analyses of treatment effectiveness.
Purpose of the Study:
- To evaluate the impact of informative censoring on COVID-19 treatment outcome analyses.
- To assess the performance of Inverse Probability of Censoring Weighting (IPCW) in correcting for bias in observational studies.
- To compare IPCW-adjusted results with traditional survival analysis methods.
Main Methods:
- Utilized a database of hospitalized COVID-19 patients, comparing those who received convalescent plasma with a control group.
- Applied Inverse Probability of Censoring Weighting (IPCW) to adjust for informative censoring.
- Employed unadjusted Kaplan-Meier curves and Cox proportional hazard models for comparative analysis.
Main Results:
- Unadjusted Kaplan-Meier curves overestimated overall mortality compared to IPCW analysis.
- Cox proportional hazard models underestimated hazard ratios for older age groups versus the youngest when not using IPCW.
- IPCW analysis demonstrated stabilizing weights, improving the reliability of estimates based on hospital admission data.
Conclusions:
- IPCW is a valuable statistical method for correcting bias due to informative censoring in COVID-19 treatment outcome studies.
- Traditional survival analyses without appropriate bias correction can lead to inaccurate estimations of mortality and risk.
- Accurate statistical methods are essential for reliable interpretation of treatment effectiveness in clinical research.
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