Predictive mean matching imputation of semicontinuous variables

Predictive mean matching imputation of semicontinuous variables

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Article ID: iaor201522331
Volume: 68
Issue: 1
Start Page Number: 61
End Page Number: 90
Publication Date: Feb 2014
Journal: Statistica Neerlandica
Authors: , , ,
Keywords: missing values
Abstract:

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.

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