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Missing data will be imputed using multiple imputation by chained equations.
Missing values were imputed using Multiple Imputation by Chained Equations package, version 1.16, for R (14).
Therefore, when establishing nonrandom study attrition, missing data are imputed, using multiple imputation by chained equations.
Missing covariate values were imputed with multiple imputation by chained equations.
Missing data values for these two variables were imputed using multiple imputation by chained equations.
In order to avoid a loss in efficiency missing values for ILI were imputed using multiple imputation by chained equations.
Missing data for some days were imputed by multiple imputation, by incorporating data from other pollution monitors as well as meteorological variables.
To minimize bias resulting from the loss of information not following a completely at random reason, missing values will be imputed using multiple imputation by chained equations.
Missing QOL observations were imputed using multiple imputation by chained equation methods [ 15, 16] using variables related to QOL including age, gender and ADL score.
All other missing values for covariates were imputed using the multiple imputation by chained equations method [ 25].
Missing data were imputed using a Multiple Imputation by Chained Equation (MICE) approach to obtain a full dataset [ 21].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com