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Values missing in the risk factors were taken into account by multiple imputations, reducing possible biases and efficiency loss.
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Missing values were taken into account by multiple imputations, to reduce possible biases and efficiency loss, assuming that data were missing at random.
Missing values will be imputed by multiple imputations assuming data are missing at random.
In HLM, the analysis of plausible values is done by multiple imputations.
Remaining missing values were replaced by multiple imputations using the expected maximum function in PRELIS.
For the regression analyses, missing values were handled by multiple imputations.
In secondary analyses, missing values will be replaced by multiple imputations using Markoff Chain Monte Carlo methods [ 78].
Missing values (ranging between 5 and 30%) were imputed by multiple imputation.
For the secondary outcome measures, missing values will be imputed by multiple imputation techniques.
Missing variables were imputed by multiple imputation (n = 5) using the mi command in Stata.
These scores were imputed by multiple imputation and included in analysis.
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