Missing data was handled using multiple imputation with imputed data generated using a sequential regression imputation method via the software package IVEware [ 31].
Missing data was imputed using multiple imputation with chained equations (MICE), where appropriate [ 28].
Missing covariate values were imputed using multiple imputation with the Markov chain Monte Carlo method for arbitrary missing data (14).
Our external validation study also used multiple imputation, with 20 imputed datasets, to deal with the missing data.
Missing values for the confounding factors (BMI, smoking status, and alcohol intake) were imputed with multiple imputation with the ICE programs in Stata.
To avoid bias, multiple imputation with 10 imputed data sets was performed for these missing data before estimation of the propensity scores, using a regression model that included all baseline characteristics.
In order to obtain unbiased estimates of the effect of each dietary pattern using the information provided by all case control pairs, missing values were imputed using multiple imputation with chained equations (Lee and Carlin, 2010; White et al, 2011).
To reduce possible bias and account for uncertainty, our analyses used the Multivariate Imputation by Chained Equations method of multiple imputation with five replications of imputed data based on when recurrence occurred between diagnosis and breast cancer death for other patients (Van Buuren et al, 1999; Royston, 2004).
Multiple imputation with chained equations was used to impute missing baseline demographic and clinical data [24].
Multiple imputation with m=10, 20 and 30 multiply imputed datasets were performed.
We used ice for multiple imputation with chained equations [ 33] for imputing missing data.
