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Missing values will be replaced using multiple imputation methods based on observed values with varying assumptions.
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Overall, there were few missing values.e Missing values in the control variables (mother tongue, commercial upper secondary school attended, final school grade, finance and mathematics courses completed) were replaced using multiple imputation; five imputations were generated for each missing value.
Missing values were replaced using multiple imputation.
Missing baseline data were replaced using multiple imputations.
Missing baseline data were replaced using multiple imputation (see online only supplementary methods).
14 Missing baseline data were replaced using multiple imputations with 20 cycles.
Due to item non-response, missing values were replaced using multiple imputation.
Missing baseline data were replaced using multiple imputations (see supplementary methods, available online only).
For all analyses, missing baseline data were replaced using multiple imputations.
Missing covariate data were replaced using multiple imputation (imputed datasets: N=30) applying multivariate chained equations as described for use in the Cox model.
All other missing values were replaced using multiple imputation with type 2 diabetes, age, sex and ethnicity as the predictor variables.
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