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Our use of a propensity score matched analysis allowed us to balance several characteristics of patients (such as age, sex, comorbidity, various indices of socioeconomic status) and providers (such as surgeon experience, teaching hospital status, hospital volume) between groups.
The study focuses on the variation in estimation of VE for both virological and clinical consultation outcomes and understanding the dependency on date of analysis during the season, methodological approach and the effect of use of a propensity score model.
Nonetheless, the use of a propensity score analysis that takes into account these differences confirmed the initial findings.
The use of a propensity score in our study could therefore underestimate the effect of albumin on the risk of developing AKI.
Therefore after multivariate adjustment including the use of a propensity score, we observed no improvement in MACE or survival in those patients who received GP IIb/IIIa inhibitors.
The use of a propensity score to match controls was used in an attempt to reduce any potential bias between controls and cases when determining CDI-attributable LOS.
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Third, the use of a propensity-based method enabled us to address the challenge of potential confounding that has hampered previous analyses.
The authors do a good job of addressing this issue, including the use of a transfusion propensity score.
In addition, data on the prevalence of diabetes in the pre- and post-PPNO populations were not optimal, but the impact was limited by the use of a more rigorous propensity score matching methodology.
Second, regardless of using a propensity score-matched analysis, our study results were not equivalent to those of a randomised controlled trial.
As patients were not randomized to the use of IORT, the authors used a propensity score adjustment in their analysis of disease control to account for differing prognostic factors between the two groups.
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