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Multiple imputation of missing values: New features for mim

Author

Listed:
  • Patrick Royston

    (MRC Clinical Trials Unit)

  • John B. Carlin

    (Murdoch Children’s Research Institute and University of Melbourne)

  • Ian R. White

    (MRC Biostatistics Unit)

Abstract

We present an update of mim, a program for managing multiply im- puted datasets and performing inference (estimating parameters) using Rubin’s rules for combining estimates from imputed datasets. The new features of particular importance are an option for estimating the Monte Carlo error (due to the sampling variability of the imputation process) in parameter estimates and in related quantities, and a general routine for combining any scalar estimate across imputations. Copyright 2009 by StataCorp LP.

Suggested Citation

  • Patrick Royston & John B. Carlin & Ian R. White, 2009. "Multiple imputation of missing values: New features for mim," Stata Journal, StataCorp LP, vol. 9(2), pages 252-264, June.
    • Handle: RePEc:tsj:stataj:v:9:y:2009:i:2:p:252-264
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    References listed on IDEAS

    as
    1. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.
    2. Nicholas J. Cox, 2008. "Speaking Stata: Correlation with confidence, or Fisher's z revisited," Stata Journal, StataCorp LP, vol. 8(3), pages 413-439, September.
    3. John B. Carlin & John C. Galati & Patrick Royston, 2008. "A new framework for managing and analyzing multiply imputed data in Stata," Stata Journal, StataCorp LP, vol. 8(1), pages 49-67, February.
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