SPECIAL SERIES: ORIGINAL ARTICLES Using the outcome for imputation of missing predictor values was preferred

By Karel G. M. Moons, Theo Stijnen, Frank E. Harrell. 2006

1,001 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8b814e13aafe17a9a2bc6f03a3e8a54cec4553bb

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Citations in the corpus

Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2 (2008, methodology): “Multiple imputation is a statistical technique designed to reduce the biases that can occur in “complete case” analysis along with a substantial loss of power and precision.”
Missing data and multiple imputation in clinical epidemiological research (2017, unknown): “Although we cannot prove whether data are MAR, it is likely that in many situations, the MAR assumption is more plausible when more variables are included in the multiple imputation model.(21,22)”
Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist (2014, background): “However, reporting on the frequency and type of missing data is often poor [2,15,56,62,78,103–105] despite the adverse effects of missing data on development, validation, and updating of a prediction model [34,103,105–112].”
Multiple imputation using chained equations: Issues and guidance for practice (2011, methodology): “In particular, when imputing missing values of analysis model covariates, the analysis model outcome must be in the imputation model [25].”
Much Ado About Nothing (2007, background): “Others have noted that incorporation of information regarding the outcome improves estimation of missing predictors (Moons et al. 2006).”
Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis (2012, methodology): “Multiple imputation provides valid estimates and standard errors in many circumstances when missing data are missing at random.(2,3,5,6,11) However, it is a complex technique requiring expertise and appropriate software.”
Multiple imputation with multivariate imputation by chained equation (MICE) package. (2016, background): “Variables included into imputation are advised to be (I) predictive of missingness, (II) associated with the variable being imputed and (III) the outcome variable of your analysis (3,4).”
MICE: Multivariate Imputation by Chained Equations in R (2011, methodology): “The method has been found to work well in a variety of simulation studies (Brand 1999; Horton and Lipsitz 2001; Moons et al. 2006; van Buuren et al. 2006b; Horton and Kleinman 2007; Yu et al. 2007; Schunk 2008; Drechsler and Rassler 2008; Giorgi et al. 2008).”