The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Computational Strategies for Multivariate Linear Mixed-Effects Models With Missing Values
Abstract
This article presents new computational techniques for multivariate longitudinal or clustered data with missing values. Current methodology for linear mixed-effects models can accommodate imbalance or missing data in a single response variable, but it cannot handle missing values in multiple responses or additional covariates. Applying a multivariate extension of a popular linear mixed-effects model, we create multiple imputations of missing values for subsequent analyses by a straightforward and effective Markov chain Monte Carlo procedure. We also derive and implement a new EM algorithm for parameter estimation which converges more rapidly than traditional EM algorithms because it does not treat the random effects as “missing data,” but integrates them out of the likelihood function analytically. These techniques are illustrated on models for adolescent alcohol use in a large school-based prevention trial.
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Cites
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Multiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072
Linear Mixed Models for Longitudinal Data
Verbeke, G. (1997) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2294-1_3 · Cited by 3,506
Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
Smith, A. F. M., & Roberts, G. O. (1993) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1993.tb01466.x · Cited by 1,713
On structural equation modeling with data that are not missing completely at random
Muthén, B., Kaplan, D., & Hollis, M. (1987) · Psychometrika · doi:10.1007/bf02294365 · Cited by 883 · cited 1×
“In panel studies where individuals are assessed at a common set of occasions, models equivalent to ours may be formulated as latent growth curves (McArdle 1988; Meredith and Tisak 1990 ) and fit with structural-equations software.”
Background
Cited by
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
“Most techniques have been developed under the joint modeling perspective (Schafer and Yucel 2002; Yucel 2008; Goldstein et al. 2009).”
MethodMissing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Self-citationMissing data analysis: making it work in the real world
Graham, J. W. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085530 · Cited by 6,163
4. Regression with Missing Ys: An Improved Strategy for Analyzing Multiply Imputed Data
von Hippel, P. T. (2007) · Sociological Methodology · doi:10.1111/j.1467-9531.2007.00180.x · Cited by 1,495
Cannabis use and mental health in young people: cohort study.
Patton, G. C., Coffey, C., Carlin, J. B., Degenhardt, L., Lynskey, M., & Hall, W. (2002) · BMJ · doi:10.1136/bmj.325.7374.1195 · Cited by 997
Metadata
- Authors
- Schafer, J. L., & Yucel, R. M.
- Venue
- Journal of Computational and Graphical Statistics
- Publisher
- Informa UK Limited
- Published
- 2002-06
- Volume
- 11
- Issue
- 2
- Pages
- 437-457
- ISSN
- 1061-8600 (ISSN-L)
- 1537-2715
- OpenAlex
- W2019391304
- Cited by
- 380
- References
- 8
- FWCI
- 6.84
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
