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We used missing at random assumptions and performed complete case analysis, in all cases except for baseline creatinine, which was required for AKI diagnosis, and was estimated using the MDRD equation [10].
To account for this, we used missing data techniques.
We used missing indicator variables in the multivariate models for covariates with missing data.
We used missing indicator variables for the small amount of missing data.
We used missing indicators for missing data on maternal smoking during pregnancy, parity, SES, and race/ethnicity.
Our first analyses used missing as a separate category of smoking, so we refer to those with reported smoking status as 'known smokers' and 'known ex-smokers'.
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Indeed, the score cannot be computed for individuals with one or more missing value, unless using missing value imputation.
The phrase that she "wants" to use ("missing from Harris's life") permits the retraction ("that isn't right") and the accompanying clarification ("They were in his life").
One may use missing value imputation to increase accuracy of predictive models.
The algorithm uses missing values as a filling alternative in order to avoid spectral artifacts at the segment boundaries.
In Part II, FAM-MDR obviously does not use missing new trait values, whether these are due to missing trait values or missing main effects/covariates adjusted for.
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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