The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Multilevel models with multivariate mixed response types
Abstract
We build upon the existing literature to formulate a class of models for multivariate mixtures of Gaussian, ordered or unordered categorical responses and continuous distributions that are not Gaussian, each of which can be defined at any level of a multilevel data hierarchy. We describe a Markov chain Monte Carlo algorithm for fitting such models. We show how this unifies a number of disparate problems, including partially observed data and missing data in generalized linear modelling. The two-level model is considered in detail with worked examples of applications to a prediction problem and to multiple imputation for missing data. We conclude with a discussion outlining possible extensions and connections in the literature. Software for estimating the models is freely available.
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Citations
Cites
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012 · cited 4×
“We then proceed as follows (see Schafer, 1997 ):”
MethodInfluentialMultiple imputation of discrete and continuous data by fully conditional specification
van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827
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).”
MethodSterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009) · BMJ · doi:10.1136/bmj.b2393 · Cited by 7,299
“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 information on partiall…”
MethodSelf-citationREALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types
Carpenter, J. R., Goldstein, H., & Kenward, M. G. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i05 · Cited by 395
Self-citationMultiple imputation as a flexible tool for missing data handling in clinical research
Enders, C. K. (2017) · Behaviour Research and Therapy · doi:10.1016/j.brat.2016.11.008 · Cited by 355
A comparison of multiple imputation methods for missing data in longitudinal studies
Huque, M. H., Carlin, J. B., Simpson, J. A., & Lee, K. J. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0615-6 · Cited by 320
Metadata
- Authors
- Goldstein, H., Carpenter, J., Kenward, M. G., & Levin, K. A.
- Venue
- Statistical Modelling
- Publisher
- SAGE Publications
- Published
- 2009-10
- Volume
- 9
- Issue
- 3
- Pages
- 173-197
- ISSN
- 1471-082X (ISSN-L)
- 1477-0342
- OpenAlex
- W2154112340
- Cited by
- 202
- References
- 2
- FWCI
- 8.20
- Type
- journal article
- Language
- en
- Open access
- closed
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- Goldstein, H., Carpenter, J., Kenward, M. G., & Levin, K. A. (2009). Multilevel models with multivariate mixed response types. Statistical Modelling. https://doi.org/10.1177/1471082x0800900301
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