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Multiple imputation of missing values

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Author Info
Patrick Royston () (MRC Clinical Trials Unit)
Abstract

Following the seminal publications of Rubin about thirty years ago, statisticians have become increasingly aware of the inadequacy of "complete-case" analysis of datasets with missing observations. In medicine, for example, observations may be missing in a sporadic way for different covariates, and a complete-case analysis may omit as many as half of the available cases. Hotdeck imputation was implemented in Stata in 1999 by Mander and Clayton. However, this technique may perform poorly when many rows of data have at least one missing value. This article describes an implementation for Stata of the MICE method of multiple multivariate imputation described by van Buuren, Boshuizen, and Knook (1999). MICE stands for multivariate imputation by chained equations. The basic idea of data analysis with multiple imputation is to create a small number (e.g., 5-10) of copies of the data, each of which has the missing values suitably imputed, and analyze each complete dataset independently. Estimates of parameters of interest are averaged across the copies to give a single estimate. Standard errors are computed according to the "Rubin rules", devised to allow for the between- and within-imputation components of variation in the parameter estimates. This article describes five ado-files. mvis creates multiple multivariate imputations. uvis imputes missing values for a single variable as a function of several covariates, each with complete data. micombine fits a wide variety of regression models to a multiply imputed dataset, combining the estimates using Rubin's rules, and supports survival analysis models (stcox and streg), categorical data models, generalized linear models, and more. Finally, misplit and mijoin are utilities to interconvert datasets created by mvis and by the miset program from John Carlin and colleagues. The use of the routines is illustrated with an example of prognostic modeling in breast cancer. Copyright 2004 by StataCorp LP.

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Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 4 (2004)
Issue (Month): 3 (September)
Pages: 227-241
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Handle: RePEc:tsj:stataj:v:4:y:2004:i:3:p:227-241

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Related research
Keywords: mvis; uvis; micombine; mijoin; misplit; missing data; missing at random; multiple imputation; multivariate imputation; regression modeling;

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. W. Sauerbrei & P. Royston, 1999. "Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials," Journal Of The Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 71-94. [Downloadable!] (restricted)
  2. Adrian Mander & David Clayton, 2000. "Hotdeck imputation," Stata Technical Bulletin, StataCorp LP, vol. 9(51). [Downloadable!]
  3. John B. Carlin & Ning Li & Philip Greenwood & Carolyn Coffey, 2003. "Tools for analyzing multiple imputed datasets," Stata Journal, StataCorp LP, vol. 3(3), pages 226-244, September. [Downloadable!]
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Krzysztof Tymicki, 2009. "The correlates of infant and childhood mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 20(23), pages 559-594, May. [Downloadable!]
  2. Denis Conniffe & Donal O’Neill, 2008. "An Efficient Estimator for Dealing with Missing Data on Explanatory Variables in a Probit Choice Model," Economics, Finance and Accounting Department Working Paper Series n1960908.pdf, Department of Economics, Finance and Accounting, National University of Ireland - Maynooth. [Downloadable!]
  3. Gabriele Beissel Durrant, 2009. "Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological Review," Working Papers id:2007, esocialsciences.com. [Downloadable!]
  4. Andrea Marshall & Lucinda Billingham & Stirling Bryan, 2009. "Can we afford to ignore missing data in cost-effectiveness analyses?," The European Journal of Health Economics, Springer, vol. 10(1), pages 1-3, February. [Downloadable!] (restricted)
  5. Conniffe, Denis & O'Neill, Donal, 2009. "Efficient Probit Estimation with Partially Missing Covariates," IZA Discussion Papers 4081, Institute for the Study of Labor (IZA). [Downloadable!]
  6. Esteban Calvo & Kelly Haverstick & Steven A. Sass, 2007. "What Makes Retirees Happier: A Gradual or 'Cold Turkey' Retirement?," Working Papers, Center for Retirement Research at Boston College wp2007-18, Center for Retirement Research, revised Oct 2007. [Downloadable!]
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  7. von Hinke Kessler Scholder, S, 2009. "*** Article withdrawn *** The Effect of Child Weight on Academic Performance: Evidence using Genetic Markers," Health, Econometrics and Data Group (HEDG) Working Papers 09/25, HEDG, c/o Department of Economics, University of York. [Downloadable!]
  8. Alessandra Mattei, 2009. "Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing," Statistical Methods and Applications, Springer, vol. 18(2), pages 257-273, July. [Downloadable!] (restricted)
  9. Christopher Paul & William Mason & Daniel McCaffrey & Sarah Fox, 2008. "A cautionary case study of approaches to the treatment of missing data," Statistical Methods and Applications, Springer, vol. 17(3), pages 351-372, July. [Downloadable!] (restricted)
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