Multiple imputation of discrete and continuous data by fully conditional specification

By S. van Buuren. 2007

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Citations in the corpus

Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, methodology): “We first use fully conditional specification (FCS) (van Buuren et al. 2006; van Buuren 2007) to estimate all missing variables.”
MissForest - non-parametric missing value imputation for mixed-type data (2011, background): “In contrast to the latter the conditional distribution for the missing data in each incomplete variable is speciﬁed in MICE, a feature called fully conditional speciﬁcation by Van Buuren (2007).”
Multiple imputation using chained equations: Issues and guidance for practice (2011, methodology): “MICE is also known as fully conditional specification [10] and sequential regression multivariate imputation [11].”
Multiple Imputation by Chained Equations: What is it and how does it work? (2011, methodology): “The chained equation approach to multiple imputation MICE is a particular multiple imputation technique (Raghunathan et al., 2001; Van Buuren, 2007).”
Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé) (2019, methodology): “…complete case analysis and multiple imputation in sensitivity analyses: multiple imputation for missing data was performed using the MICE method 58 by fully conditional specification (20 imputed datasets) for the outcome 59 and for several covariates: level of education (5.0% missing data),…”
Multiple imputation using chained equations for missing data in TIMSS: a case study (2013, positive): “If the magnitude of a correlation exceeds a certain level, then the applicable variable is included (Van Buuren et al., 1999). Allison (2002), Moons, Donders, Stijnen, and Harrell (2006), and White et al. (2011) all highlight the need to include the dependent variable of the analysis model in the im”
Flexible Imputation of Missing Data (2012, methodology): “This technique is also useful for calculating scale scores from imputed questionnaire items (Van Buuren, 2010). It is easy to specify such models in mice. See Van Buuren and Groothuis-Oudshoorn (2011) for examples.”
Combining Multiple Imputation and Inverse-Probability Weighting (2012, methodology): “Thomas et al., using the ice command in STATA (Royston, 2005), performed MI by chained equations (Van Buuren, 2007) on the 7518 subjects, producing 10 complete datasets.”
MICE: Multivariate Imputation by Chained Equations in R (2011, methodology): “FCS is attractive as an alternative to JM in cases where no suitable multivariate distribution can be found.”
Multiple Imputation by Chained Equations (MICE): Implementation in Stata (2011, background): “It is straightforward to specify a set of incompatible conditional distributions for which no multivariate density exists (van Buuren 2007). In general, little is known theoretically or practically about the effects of incompatible conditional distributions on the quality of imputations. However, va”
Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model (2012, neutral): “Despite the fact that FCS MI has been applied widely in a number of ﬁelds, 5 until recently few results were available regarding its validity.”