IDEAS home Printed from https://ideas.repec.org/a/tsj/stataj/v15y2015i2p437-456.html
   My bibliography  Save this article

Multiple imputation of covariates by substantive-model compatible fully conditional specification

Author

Listed:
  • Jonathan W. Bartlett

    (London School of Hygiene and Tropical Medicine)

  • Tim P. Morris

    (MRC Clinical Trials Unit at UCL)

Abstract

Multiple imputation is a practical, principled approach to handling missing data. When used to impute missing values in covariates of regression models, imputation models may be misspecified if they are not compatible with the substantive model of interest for the outcome. In this article, we introduce the smcfcs command, which imputes covariates by substantive-model compatible fully conditional specification. This modifies the popular fully conditional specification or chained-equations approach to multiple imputation by imputing each covariate compatibly with a user-specified substantive model. We compare the smcfcs command with standard fully conditional specification imputation using mi impute chained in a simulation study and illustrative analysis of data from a study investigating time to tumor recurrence in breast cancer. Copyright 2015 by StataCorp LP.

Suggested Citation

  • Jonathan W. Bartlett & Tim P. Morris, 2015. "Multiple imputation of covariates by substantive-model compatible fully conditional specification," Stata Journal, StataCorp LP, vol. 15(2), pages 437-456, June.
    • Handle: RePEc:tsj:stataj:v:15:y:2015:i:2:p:437-456
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj15-2/st0387/
    as

    Download full text from publisher

    File URL: http://www.stata-journal.com/article.html?article=st0387
    File Function: link to article purchase
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Lauren J. Beesley & Jeremy M. G. Taylor, 2021. "A stacked approach for chained equations multiple imputation incorporating the substantive model," Biometrics, The International Biometric Society, vol. 77(4), pages 1342-1354, December.
    2. Lily Clements & Alan C. Kimber & Stefanie Biedermann, 2022. "Multiple Imputation of Composite Covariates in Survival Studies," Stats, MDPI, vol. 5(2), pages 1-13, March.

    Corrections

    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:tsj:stataj:v:15:y:2015:i:2:p:437-456. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum or Lisa Gilmore (email available below). General contact details of provider: http://www.stata-journal.com/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.