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.
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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-citationHorton, N. J., & Lipsitz, S. R. (2001) · The American Statistician · doi:10.1198/000313001317098266 · Cited by 600
Wood, A. M., White, I. R., & Thompson, S. G. (2004) · Clinical Trials · doi:10.1191/1740774504cn032oa · Cited by 543
Self-citationSauerbrei, W., & Royston, P. (1999) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/1467-985x.00122 · Cited by 475
Self-citationMultiple imputation: current perspectives
Kenward, M. G., & Carpenter, J. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206075304 · Cited by 456
Practical Statistics for Medical Research
BROWN, G. W. (1992) · Archives of Pediatrics & Adolescent Medicine · doi:10.1001/archpedi.1992.02160180040013 · Cited by 448
Performing likelihood ratio tests with multiply-imputed data sets
MENG, X.-L., & RUBIN, D. B. (1992) · Biometrika · doi:10.1093/biomet/79.1.103 · Cited by 366
Intensive versus standard case management for severe psychotic illness: a randomised trial
Burns, T., Creed, F., Fahy, T., Thompson, S., & Tyrer, P. (1999) · The Lancet · doi:10.1016/s0140-6736(98)12191-8 · Cited by 330
Bootstrap investigation of the stability of a cox regression model
Altman, D. G., & Andersen, P. K. (1989) · Statistics in Medicine · doi:10.1002/sim.4780080702 · Cited by 310
The Use of Resampling Methods to Simplify Regression Models in Medical Statistics
Sauerbrei, W. (1999) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/1467-9876.00155 · Cited by 310
Mathematical Methods for Digital Computers.
Way, F., Ralston, A., & Wilf, H. S. (1961) · The American Mathematical Monthly · doi:10.2307/2312521 · Cited by 255
Sensitivity analysis after multiple imputation under missing at random: a weighting approach
Carpenter, J. R., Kenward, M. G., & White, I. R. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206075303 · Cited by 233
Self-citationNot Asked and Not Answered: Multiple Imputation for Multiple Surveys
Gelman, A., King, G., & Liu, C. (1998) · Journal of the American Statistical Association · doi:10.1080/01621459.1998.10473737 · Cited by 141
Royston, P. J., & Sauerbrei, W. (2003) · Statistics in Medicine · doi:10.1002/sim.1310 · Cited by 124
Self-citationMultiple Imputation for Model Checking: Completed‐Data Plots with Missing and Latent Data
Gelman, A., Mechelen, I. V., Verbeke, G. N., Heitjan, D. F., & Meulders, M. (2005) · Biometrics · doi:10.1111/j.0006-341x.2005.031010.x · Cited by 121
Evaluation of software for multiple imputation of semi-continuous data
Yu, L., Burton, A., & Rivero‐Arias, O. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074464 · Cited by 107
Imputation and Variable Selection in Linear Regression Models with Missing Covariates
Yang, X., Belin, T. R., & Boscardin, W. J. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00317.x · Cited by 78
Multiple imputation of missing blood pressure covariates in survival analysis
van Buuren, S., Boshuizen, H. C., & Knook, D. L. (1999) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19990330)18:6<681::aid-sim71>3.0.co;2-r
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-citation
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
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