Advanced Search
MyIDEAS: Login to save this article or follow this journal

Multiple imputation of missing values

Contents:

Author Info

  • Patrick Royston

    ()
    (MRC Clinical Trials Unit)

Registered author(s):

    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.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.stata-journal.com/software/sj4-3/st0067/
    Download Restriction: no

    File URL: http://www.stata-journal.com/sjpdf.html?articlenum=st0067
    Download Restriction: no

    Bibliographic Info

    Article provided by StataCorp LP in its journal Stata Journal.

    Volume (Year): 4 (2004)
    Issue (Month): 3 (September)
    Pages: 227-241

    as in new window
    Handle: RePEc:tsj:stataj:v:4:y:2004:i:3:p:227-241

    Note: Windows users should not attempt to download these files with a web browser.
    Contact details of provider:
    Web page: http://www.stata-journal.com/

    Order Information:
    Web: http://www.stata-journal.com/subscription.html

    Related research

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

    References

    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.:
    as in new window
    1. 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.
    2. 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.
    3. Adrian Mander & David Clayton, 2000. "Hotdeck imputation," Stata Technical Bulletin, StataCorp LP, vol. 9(51).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as in new window

    Cited by:
    This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:tsj:stataj:v:4:y:2004:i:3:p:227-241. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum) or (Lisa Gilmore).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.