Multiple imputations for missing data in lifecourse studies

Missing imputation (MI) is a method to deal with missing at random (MAR) data. It is a Monte Carlo procedure where missing values are replaced by several (usually less than 10) simulated versions. It consists of three steps (Shafer, 1999): i. generation of the imputed values for the missing data; ii. analysis of each imputed data set where missing observations are replaced by imputed ones; iii. combination of the results from all imputed data sets. The procedure is easily implemented in Stata for univariate normally distributed missing variables. Extensions to the case of multivariate normal variables - often encountered in life course epidemiology - will be discussed.