Computational Strategies for Multivariate Linear Mixed-Effects Models With Missing Values

By Joseph L. Schafer, Recai M. Yucel. 2002

360 citations recorded by the source corpus.

Subfields: Environmental Engineering, Statistics and Probability

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Citations in the corpus

Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, methodology): “Since we expect the mean of the symptom measure PANSS to be smooth across time, we use a second method, based on a mixed effects model (Schafer 1997; Schafer and Yucel 2002 ), to re-impute individual monthly PANSS scores according to the desired smoothness constraint.”
Multiple imputation as a flexible tool for missing data handling in clinical research. (2017, neutral): “…data handling methods for multilevel data structures (Drechsler, 2015; Enders et al., 2016; Enders et al., 2016; Goldstein, Bonnet, & Rocher, 2007; Goldstein, Carpenter, Kenward, & Levin, 2009; Grund, Lü;dke, & Robitzsch, 2016; Schafer & Yucel, 2002; Shin & Raudenbush, 2007; Yucel, 2008, 2011).”
Flexible Imputation of Missing Data (2012, methodology): “Schafer implements multiple imputation for multivariate panel or clustered data using the linear mixed model (Schafer and Yucel, 2002).”
Multiple Imputation of Missing Data for Multilevel Models (2018, methodology): “Here, we consider the JM approach implemented in the pan package (Schafer & Yucel, 2002) and the FCS approach known as “multiple imputation by chained equations” implemented in the mice package (van Buuren & Groothuis-Oudshoorn, 2011) in the statistical software R (R Core Team, 2016).”
3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data (2017, neutral): “Works by Liu et al. 27 and by Schafer and Yucel 28 extended multiple imputation approaches based on the linear mixed model to longitudinal data.”
The Causal Effect of Class Size on Academic Achievement (2011, methodology): “…model-based missing data methods at a single level (Dempster, Laird, & Rubin, 1977; Little & Rubin, 2002; Orchard & Woodbury, 1972; Rubin, 1976, 1987; Schafer, 1997) and at two levels (Dempster, Rubin, & Tsutakawa, 1981; Liu, Taylor, & Belin, 2000; Schafer & Yucel, 2002; Shin & Raudenbush, 2007).”
Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity (2015, methodology): “For this reason, different strategies to setup multilevel imputation models have been discussed in the literature (Liu, Taylor, & Belin, 1995; Schafer, 1997; Schafer & Yucel, 2002; Yucel, 2011).”
Regression with missing Ys: An improved strategy for analyzing multiply imputed data (2007, methodology): “putation software would treat the cluster indicators as fixed effects, which leads to estimation problems in the ECLS-K since the clusters are small with many missing values (Reiter and Raghunathan). Schafer and Yucel (2002) develop a method that accounts for clustering in a random-effects framework”
Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: a simulation assessment (2008, methodology): “Several strategies could be employed to determine the hyperparameters, such as using data to obtain prior ‘guesses’ on the parameters [7, 15] or via previous studies.”
Missing data techniques for multilevel data: implications of model misspecification (2011, background): “where Wi represents level-2 covariates, Xi represents level-1 covariates, and β and b represent fixed effects and random effects, respectively [28,33].”
MICE: Multivariate Imputation by Chained Equations in R (2011, methodology): “Most techniques have been developed under the joint modeling perspective (Schafer and Yucel 2002; Yucel 2008; Goldstein et al. 2009).”