The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Multiple Imputation for Multilevel Data with Continuous and Binary Variables
Open accessAbstract
We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. The comparisons show that these multiple imputation methods are the most appropriate to handle missing values in a multilevel setting and why their relative performances can vary according to the missing data pattern, the multilevel structure and the type of missing variables. This study shows that valid inferences can only be obtained if the dataset includes a large number of clusters. In addition, it highlights that heteroscedastic multiple imputation methods provide more accurate inferences than homoscedastic methods, which should be reserved for data with few individuals per cluster. Finally, guidelines are given to choose the most suitable multiple imputation method according to the structure of the data.
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Cites
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
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
Self-citationRUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Mixed-Effects Models in S and S-PLUS
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Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
Analysis of Incomplete Multivariate Data
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Multiple 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
Allison, P. (2002) · doi:10.4135/9781412985079 · Cited by 2,050
Fully conditional specification in multivariate imputation
Van Buuren, S., Brand, J. P. L., Groothuis-Oudshoorn, C. G. M., & Rubin, D. B. (2006) · Journal of Statistical Computation and Simulation · doi:10.1080/10629360600810434 · Cited by 1,217
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Using simulation studies to evaluate statistical methods
Morris, T. P., White, I. R., & Crowther, M. J. (2019) · Statistics in Medicine · doi:10.1002/sim.8086 · Cited by 1,305
Self-citationMissing Data in Clinical Research: A Tutorial on Multiple Imputation
Austin, P. C., White, I. R., Lee, D. S., & van Buuren, S. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2020.11.010 · Cited by 1,006
Self-citationMethodological guidance paper: High-quality meta- analysis in a systematic review
Pigott, T. D., & Polanin, J. R. (2020) · Review of Educational Research · doi:10.3102/0034654319877153 · Cited by 568
Pilot randomized trial of self-guided virtual reality exposure therapy for social anxiety disorder
Zainal, N. H., Chan, W. W., Saxena, A. P., Taylor, C. B., & Newman, M. G. (2021) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.103984 · Cited by 84
Methods to Address Confounding and Other Biases in Meta-Analyses: Review and Recommendations
Mathur, M. B., & VanderWeele, T. J. (2022) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-051920-114020 · Cited by 85
Metadata
- Authors
- Audigier, V., White, I. R., Jolani, S., Debray, T. P. A., Quartagno, M., Carpenter, J., van Buuren, S., & Resche-Rigon, M.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 2018-05-01
- Volume
- 33
- Issue
- 2
- Pages
- 160-183
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W2586627884
- Cited by
- 166
- References
- 17
- FWCI
- 14.31
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Audigier, V., White, I. R., Jolani, S., Debray, T. P. A., Quartagno, M., Carpenter, J., van Buuren, S., & Resche-Rigon, M. (2018). Multiple Imputation for Multilevel Data with Continuous and Binary Variables. Statistical Science. https://doi.org/10.1214/18-sts646
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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.
