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Firstly, the analysis was performed after multiple imputation of missing data using multiple imputation chained equation methods.
To confirm results, we performed imputation of missing data using the multiple imputation procedure from SAS Institute Inc [ 18].
We were able to score all the scales in 90% of the cases without using any imputation of missing data, which increased to 97% after imputation of missing data.
Imputation of missing data Repetition of the regression analysis after imputation of missing data confirmed our findings, though with lower values for odds ratios but narrower confidence intervals.
Data were reanalyzed using regression imputation of missing data instead of last observation carried forward (4 imputations, R-square = 0.93).
Sensitivity of the results to imputation of missing data was explored by running the models with and without imputation.
However, true ITT analysis requires appropriate assumptions and imputation of missing data.
Imputation of missing data was not necessary and the cases were removed.
No imputation of missing data will be done to satisfy eligibility criteria.
Minor differences are probably due to the multiple imputation of missing data in our study.
There was no imputation for missing data, except for data missing from comorbidities, which were then considered as absent.
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