Multiple imputation methods properly account for the uncertainty of missing data. One of those methods for creating multiple imputations is predictive mean matching (PMM), a general purpose method. Little is known about the performance of PMM in imputing non-normal semicontinuous data (skewed data with a point mass at a certain value and otherwise continuously distributed). We investigate the performance of PMM as well as dedicated methods for imputing semicontinuous data by performing simulation studies under univariate and multivariate missingness mechanisms. We also investigate the performance on real-life datasets. We conclude that PMM performance is at least as good as the investigated dedicated methods for imputing semicontinuous data and, in contrast to other methods, is the only method that yields plausible imputations and preserves the original data distributions.

Clinical outcomes remain excellent up to 2 years after DMEK, in particular for eyes operated on for FECD and with completely attached grafts.

Dr. Melles is a consultant to D.O.R.C. International/Dutch Ophthalmic USA. No author has a financial or proprietary interest in any material or method mentioned.