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We explored the effect of missing data by fitting multiple imputation models under the missing-at-random (MAR) assumption (Carpenter and Kenward, 2013), and then compared the results with a complete case analysis.
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Values were imputed by using multiple imputation methods.
Missing data for income was imputed by using multiple imputation [ 17].
Missing values will be imputed by using multiple imputation (MI) methods.
Missing lipid and smoking data were imputed by using multiple imputation methods (online-only Data Supplement).
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.
Missing data by unit non-response was handled by Multiple Imputation (MI) (m = 5).
This will be addressed by the multiple imputation method.
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