Multilevel models with multivariate mixed response types

By Harvey Goldstein, James R. Carpenter, Michael G. Kenward, K. Levin. 2009

183 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ddafc0203e1595eef8957cac9bf69ce790f3b1ea

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Multiple Imputation of Missing Data for Multilevel Models (2018, methodology): “Specifically, imputations for group-level missing data can be obtained by conditioning on observed group-level variables and on the between-group components of individual-level variables by employing the same general paradigm that is already employed for multilevel MI (for details, see Carpenter & K”
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).”
REALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types (2011, methodology): “For full details of this model see Goldstein et al. (2009). This extended model remains equivalent to a conditional model for each (latent) variable in which it is linearly regressed on all the other variables.”
Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls (2009, methodology): “If we assume data are missing at random (box 1), then unbiased and statistically more powerful analyses (compared with analyses based on complete cases) can generally be done by including individuals with incomplete data. Sometimes this is possible by building a more general model incorporating info”
Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non‐linear terms (2014, methodology): “Keywords: Endogeneity; Latent normal model; Markov chain Monte Carlo methods; Missing data; Multilevel modelling; Multiple imputation; Multiprocess model; Multivariate modelling”
Strategy for Modelling Nonrandom Missing Data Mechanisms in Observational Studies Using Bayesian Methods (2012, background): “By creating an underlying set of latent variables, models for mixtures of binary, categorical and continuous variables can be developed (Dunson 2000; Goldstein et al. 2009).”