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Missing data were imputed using multiple imputations with differences in baseline quality of life and gender adjusted using regression techniques.
We used multiple imputations with chained equations to address missing data [ 15].
To address the issue of missing data, we used multiple imputations with our adjusted models (15).
14 Missing baseline data were replaced using multiple imputations with 20 cycles.
Missing data were treated using multiple imputations with 5 times imputations.
To handle missing values in covariates (<20% missing), we performed multiple imputations with a software package by generating 5 independent datasets for all analyses.
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Missing data was handled using multiple imputation with imputed data generated using a sequential regression imputation method via the software package IVEware [ 31].
Missing data was imputed using multiple imputation with chained equations (MICE), where appropriate [ 28].
Our external validation study also used multiple imputation, with 20 imputed datasets, to deal with the missing data.
Missing covariate values were imputed using multiple imputation with the Markov chain Monte Carlo method for arbitrary missing data (14).
Missing values for the confounding factors (BMI, smoking status, and alcohol intake) were imputed with multiple imputation with the ICE programs in Stata.
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