IDEAS home Printed from
   My bibliography  Save this paper

Multiple imputation with large proportions of missing data: How much is too much?


  • Jin Hyuk Lee

    (Texas A&M Health Science Center)

  • John Huber Jr.


Multiple imputation (MI) is known as an effective method for handling missing data. However, it is not clear that the method will be effective when the data contain a high percentage of missing observations on a variable. This study examines the effectiveness of MI in data with 10% to 80% missing observations using absolute bias and root mean squared error of MI measured under missing completely at random, missing at random, and not missing at random assumptions. Using both simulated data drawn from multivariate normal distribution and example data from the Predictive Study of Coronary Heart Disease, the bias and root mean squared error using MI are much smaller than of the results when complete case analysis is used. In addition, the bias of MI is consistent regardless of increasing imputation numbers (M) from M = 10 to M = 50. Moreover, compared to the regression method and predictive mean matching method, the Markov chain Monte Carlo method can also be used for continuous and univariate missing variables as an imputation mechanism. In conclusion, MI produces less-biased estimates, but when large proportions of data are missing, other things need to be considered such as the number of imputations, imputation mechanisms, and missing data mechanisms for proper imputation.

Suggested Citation

  • Jin Hyuk Lee & John Huber Jr., 2011. "Multiple imputation with large proportions of missing data: How much is too much?," United Kingdom Stata Users' Group Meetings 2011 23, Stata Users Group.
  • Handle: RePEc:boc:usug11:23

    Download full text from publisher

    File URL:
    Download Restriction: no

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:usug11:23. 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). General contact details of provider: .

    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.

    We have no references for this item. You can help adding them by using 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.