Title
Predictive mean matching imputation of semicontinuous variables
Author
Vink, G.
Frank, L.E.
Pannekoek, J.
van Buuren, S.
Publication year
2014
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. © 2014 The Authors.
Subject
Behavioural Changes
LS - Life Style
ELSS - Earth, Life and Social Sciences
Healthy for Life
Health
Healthy Living
Multiple imputation
Point mass
Predictive mean matching
Semicontinuous data
Skewed data
To reference this document use:
http://resolver.tudelft.nl/uuid:ec322ef4-f3ea-4e05-9ab0-af7f17d41018
DOI
https://doi.org/10.1111/stan.12023
TNO identifier
489143
ISSN
0039-0402
Source
Statistica Neerlandica, 68 (1), 61-90
Document type
article