The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
How should variable selection be performed with multiply imputed data?
Abstract
Multiple imputation is a popular technique for analysing incomplete data. Given the imputed data and a particular model, Rubin's rules (RR) for estimating parameters and standard errors are well established. However, there are currently no guidelines for variable selection in multiply imputed data sets. The usual practice is to perform variable selection amongst the complete cases, a simple but inefficient and potentially biased procedure. Alternatively, variable selection can be performed by repeated use of RR, which is more computationally demanding. An approximation can be obtained by a simple ‘stacked’ method that combines the multiply imputed data sets into one and uses a weighting scheme to account for the fraction of missing data in each covariate. We compare these and other approaches using simulations based around a trial in community psychiatry. Most methods improve on the naïve complete‐case analysis for variable selection, but importantly the type 1 error is only preserved if selection is based on RR, which is our recommended approach. Copyright © 2008 John Wiley & Sons, Ltd.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences.
Henry, N. W., Cohen, J., & Cohen, P. (1977) · Contemporary Sociology · doi:10.2307/2064799 · Cited by 7,089
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012
Multiple Imputation of Missing Values
Royston, P. (2004) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0400400301 · Cited by 2,340
Self-citationStatistical Analysis with Missing Data
Bo., N., Little, R. J. A., & Rubin, D. B. (1988) · Population (French Edition) · doi:10.2307/1533221 · Cited by 2,095
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
Multiple Imputation of Missing Values: Update of Ice
Royston, P. (2005) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0500500404 · Cited by 1,041
Self-citationRegression with Missing X 's: A Review
Little, R. J. A. (1992) · Journal of the American Statistical Association · doi:10.1080/01621459.1992.10476282 · Cited by 968
Derksen, S., & Keselman, H. J. (1992) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.1992.tb00992.x · Cited by 771
Multiple Imputation of Missing Values: Update
Royston, P. (2005) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0500500204 · Cited by 754
Self-citation
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
“Several strategies have been proposed that count the number of times that variable is in the model (Brand 1999; Heymans et al. 2007; Wood et al. 2008).”
BackgroundMultiple imputation using chained equations: Issues and guidance for practice
White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792
Self-citationMoons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
“These adverse effects are related to the amount of missing data [112] and the extent to which data are missing completely at random [108,111].”
BackgroundBartlett, J. W., Seaman, S. R., White, I. R., Carpenter, J. R., & Initiative*, F. T. A. D. N. (2015) · Statistical Methods in Medical Research · doi:10.1177/0962280214521348 · Cited by 485
Self-citationGeneralized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments
Wang, M. (2014) · Advances in Statistics · doi:10.1155/2014/303728 · Cited by 313
Development and validation of a prediction model with missing predictor data: a practical approach
Vergouwe, Y., Royston, P., Moons, K. G. M., & Altman, D. G. (2010) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.03.017 · Cited by 287
Self-citationDiagnostic and prognostic prediction models
Hendriksen, J. M. T., Geersing, G. J., Moons, K. G. M., & de Groot, J. A. H. (2013) · Journal of Thrombosis and Haemostasis · doi:10.1111/jth.12262 · Cited by 229
Boursi, B., Finkelman, B., Giantonio, B. J., Haynes, K., Rustgi, A. K., Rhim, A. D., Mamtani, R., & Yang, Y.-X. (2017) · Gastroenterology · doi:10.1053/j.gastro.2016.11.046 · Cited by 202
Jensen, J. F., Egerod, I., Bestle, M. H., Christensen, D. F., Elklit, A., Hansen, R. L., Knudsen, H., Grode, L. B., & Overgaard, D. (2016) · Intensive Care Medicine · doi:10.1007/s00134-016-4522-1 · Cited by 150
Walters, K., Hardoon, S., Petersen, I., Iliffe, S., Omar, R. Z., Nazareth, I., & Rait, G. (2016) · BMC Medicine · doi:10.1186/s12916-016-0549-y · Cited by 144
Clift, A. K., Dodwell, D., Lord, S., Petrou, S., Brady, M., Collins, G. S., & Hippisley-Cox, J. (2023) · BMJ · doi:10.1136/bmj-2022-073800 · Cited by 97
Multiple imputation of multiple multi-item scales when a full imputation model is infeasible
Plumpton, C. O., Morris, T., Hughes, D. A., & White, I. R. (2016) · BMC Research Notes · doi:10.1186/s13104-016-1853-5 · Cited by 76
“We also compared variable selection using complete case data with variable selection procedure using Rubin’s rules in the pooled MI data, using unadjusted or ageadjusted analyses as described above [20, 26].”
MethodSelf-citationWood, A. M., Royston, P., & White, I. R. (2015) · Biometrical Journal · doi:10.1002/bimj.201400004 · Cited by 78
Self-citationHelmhout, P. H., Staal, J. B., Heymans, M. W., Harts, C. C., Hendriks, E. J. M., & de Bie, R. A. (2010) · European Spine Journal · doi:10.1007/s00586-009-1254-8 · Cited by 71
A stacked approach for chained equations multiple imputation incorporating the substantive model
Beesley, L. J., & Taylor, J. M. G. (2021) · Biometrics · doi:10.1111/biom.13372 · Cited by 43
Niles, A. N., Wolitzky-Taylor, K. B., Arch, J. J., & Craske, M. G. (2017) · Behaviour Research and Therapy · doi:10.1016/j.brat.2017.01.001 · Cited by 26
Severe falciparum malaria in pregnancy in Southeast Asia: a multi-centre retrospective cohort study
Saito, M., Phyo, A. P., Chu, C., Proux, S., Rijken, M. J., Beau, C., Win, H. H., Archasuksan, L., Wiladphaingern, J., Phu, N. H., et al. (2023) · BMC Medicine · doi:10.1186/s12916-023-02991-8 · Cited by 14
Pettersen, R., Omerov, P., Steineck, G., Titelman, D., Dyregrov, A., Nyberg, T., & Nyberg, U. (2015) · Crisis · doi:10.1027/0227-5910/a000312 · Cited by 4
Metadata
- Authors
- Wood, A. M., White, I. R., & Royston, P.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2008-01-17
- Volume
- 27
- Issue
- 17
- Pages
- 3227-3246
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2026044573
- Cited by
- 485
- References
- 27
- FWCI
- 8.69
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.
