SPECIAL SERIES: MISSING DATA Review: A gentle introduction to imputation of missing values

By A. Rogier, T. Donders, Theo Stijnen. 2006

2,564 citations recorded by the source corpus.

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Citations in the corpus

AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records (2020, methodology): “This missing-indicator method has the appealing property that all available information can be used in the analyses [38].”
A review of the application of machine learning in water quality evaluation (2022, methodology): “Data cleaning can be handled in different ways, including not using the set of data directly, using averages or medians, or using a combination of machine learning and matrix completion methods to supplement the raw data [35].”
A survey on missing data in machine learning (2021, methodology): “The non-missing values data set is normally used to predict the values used to replace the missing values [8].”
The proportion of missing data should not be used to guide decisions on multiple imputation (2019, background): “Readers may wish to refer to the studies by Graham [2] and Donders et al [3] for intuitive explanations of these terms.”
Developing prediction models for clinical use using logistic regression: an overview. (2019, background): “This avoids the biases inherent with simply removing subjects with missing data (3,10,12).”
Multiple imputation with multivariate imputation by chained equation (MICE) package. (2016, background): “, overestimation of precision) and ignorance of uncertainty about estimation of missing values, MI, if performed properly can give an accurate estimate of real result (1).”