Estimating Causal Effect of Two-Dose COVID-19 Vaccination on Hospitalization
A Propensity Score Matching Approach
Abstract
Objective: To estimate the effectiveness of two-dose COVID-19 vaccination in reducing hospitalization, accounting for complex confounding factors in observational studies.
Methods: Researchers applied propensity score methods to adjust for confounding variables, comparing their performance to traditional covariate adjustment methods. Multiple Logistic Regression and Propensity Score Matching were employed to analyze the data, ensuring a balanced comparison between vaccinated and unvaccinated groups.
Results: Both analytical methods demonstrated a significant reduction in the likelihood of hospitalization among vaccinated individuals. The adjusted odds ratios (OR) were 0.29 (95% CI: 0.26, 0.31) via logistic regression and 0.32 (95% CI: 0.30, 0.34) using propensity score matching.
Conclusions: The study confirms the effectiveness of two-dose COVID-19 vaccination in decreasing hospitalization. It highlights the importance of using meticulous approaches like propensity score methods to assess real-world impacts in complex observational data settings.
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Issue | Vol 10 No 1 (2024) | |
Section | Articles | |
DOI | https://doi.org/10.18502/jbe.v10i1.17158 | |
Keywords | ||
propensity score matching causal effect observational study logistic regression |
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