1Analyses conducted on multiple imputed questionnaire items (imputation with Expectation-Maximization method).
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.
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.
Statistical imputation with information from correlated flanking SNPs mostly imputes these groups of missings as AA and AB respectively.
We used multiple imputation with the Markov Chain Monte Carlo method (SAS/STAT 9.2, PROC MI) to impute the missing weights and heights.
Hence, we only focus on imputation with decorrelated models.
