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We also created an additional 25(OH D category variable of <25, 25 74.9 and ≥75 nmol/l to summarize the effect size and repeated analyses for model 3. Missing information on the lifestyle factors (n = 514 with missing information on one or more factors) was imputed using the multiple imputation chained equations [47].
Missing values were imputed using the multiple imputation option in SPSS 21.
Missing data will be imputed using the multiple imputation routines in STATA v11.
Missing data will be imputed using the multiple imputation routines in STATA version 11.0 [ 57].
If judged appropriate, missing data will be imputed, using the multiple imputation facilities available in R (http://www.r-project.org/).org/
If judged appropriate, missing data will be imputed using the multiple imputation facilities (eg, mice in R).
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For these variables, to allow for the missing data, multiple imputations (10 imputations) were carried out using the Multiple Imputations by Chained Equations with predicted mean matching.
The authors used the multiple imputation procedure in SAS statistical software (PROC MI) to impute the missing data.
We used the multiple imputation method with the Markov Chain Monte Carlo algorithm to impute missing values [ 34].
We used the multiple imputation procedure in SAS statistical software (SAS Proc MI, SAS Inst., Cary, NC) to impute missing data.
Since we had missing data, we used the multiple imputation quit rate as our primary outcome.
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