title: [Imputation of missing data].
abstract: In medical research missing data are sometimes inevitable. Different missingness mechanisms can be distinguished: (a) missing completely at random; (b) missing by design; (c) missing at random, and (d) missing not at random. If participants with missing data are excluded from statistical analyses, this can lead to biased study results and loss of statistical power. Imputation methods can be applied to estimate missing values; multiple imputation gives a good idea of the inaccuracy of the reconstructed measurements. The most common imputation methods assume that missing data are missing at random. Multiple imputation contributes greatly to the efficiency and reliability of estimates because maximum use is made of the data collected. Imputation is not meant to obviate low-quality data.
authors: LUMC, afd. Klinische Epidemiologie, Leiden, the Netherlands.
authors: Ralph C A
authors: RC
authors: Rippe
authors: Martin
authors: M
authors: den Heijer
authors: Saskia
authors: S
authors: le Cessie
mesh_terms: D015982:Bias; D003627:Data Interpretation, Statistical; D006801:Humans; D015203:Reproducibility of Results; D012107:Research Design