  Including a large number of predictors in the imputation model underlying a
multiple imputation (MI) procedure is one of the most challenging tasks
imputers face. A variety of high-dimensional MI techniques can help, but there
has been limited research on their relative performance. In this study, we
investigated a wide range of extant high-dimensional MI techniques that can
handle a large number of predictors in the imputation models and general
missing data patterns. We assessed the relative performance of seven
high-dimensional MI methods with a Monte Carlo simulation study and a
resampling study based on real survey data. The performance of the methods was
defined by the degree to which they facilitate unbiased and confidencevalid
estimates of the parameters of complete data analysis models. We found that
using lasso penalty or forward selection to select the predictors used in the
MI model and using principal component analysis to reduce the dimensionality of
auxiliary data produce the best results.
