Multiple imputation using chained equations: Issues and guidance for practice

By Ian R. White, Patrick Royston, Angela M. Wood. Statistics in Medicine, 2011

8,899 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fb9dbfff9f4bfd90bfe1f09b48ce4929874fee12

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Citations in the corpus

A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM risk-SCD). (2014, methodology): “All predictors of missingness were included in the multiple imputation model, together with the outcome, all pre-specified predictors of the risk model, and the estimate of the cumulative hazard function.(37) A total of 25 imputed data sets were generated and the estimates were combined using Rubin’”
Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis (2012, methodology): “For multiple imputation we used predictive mean matching with the dichotomized difference in calf circumference and deep venous thrombosis status included in a linear regression imputation model, and 25 imputed datasets were produced.(18) We analyzed each imputed dataset using logistic regression of”
Partially Generative Neural Networks for Gang Crime Classification with Partial Information (2018, methodology): “For instance, wavelet variance analysis [19], correlation analysis [22], sequential regression [21], and multiple imputation [28] have been proposed to impute missing values.”
The proportion of missing data should not be used to guide decisions on multiple imputation (2019, background): “These are included to make the MAR assumption (required in the standard implementation of MI to produce unbiased estimates) more plausible and to provide information about the missing values [11].”
Principled missing data methods for researchers (2013, methodology): “One has to turn to numerical simulation methods, such as MCMC (Schafer 1997) in this case.”
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models (2022, methodology): “On the PEMS-Bay data set, SSSDS4 outperformed well-established baselines such as MICE [49], rGAIN [35], BRITS [5], and MPGRU [24] in terms of all three metrics MAE, MSE, and MRE, while it was only superseded by the recently proposed GRIN [9].”
An algorithm for removing sensitive information: Application to race-independent recidivism prediction (2017, methodology): “To do so, we deﬁne the problem in terms of a chain of conditional models, as is commonly used in multiple imputation (see White et al. [2011] for an overview).”