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

Robust analysis of longitudinal data with nonignorable missing responses

Contents:

Author Info

  • Sanjoy Sinha

    ()

Registered author(s):

    Abstract

    We encounter missing data in many longitudinal studies. When the missing data are nonignorable, it is important to analyze the data by incorporating the missing data mechanism into the observed data likelihood function. The classical maximum likelihood (ML) method for analyzing longitudinal missing data has been extensively studied in the literature. However, it is well-known that the ordinary ML estimators are sensitive to extreme observations or outliers in the data. In this paper, we propose and explore a robust method, which is developed in the framework of the ML method, and is useful for downweighting any influential observations in the data when estimating the model parameters. We study the empirical properties of the robust estimators in small simulations. We also illustrate the robust method using incomplete longitudinal data on CD4 counts from clinical trials of HIV-infected patients. Copyright Springer-Verlag 2012

    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://hdl.handle.net/10.1007/s00184-011-0359-3
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by Springer in its journal Metrika.

    Volume (Year): 75 (2012)
    Issue (Month): 7 (October)
    Pages: 913-938

    as in new window
    Handle: RePEc:spr:metrik:v:75:y:2012:i:7:p:913-938

    Contact details of provider:
    Web page: http://www.springerlink.com/link.asp?id=102509

    Order Information:
    Web: http://link.springer.de/orders.htm

    Related research

    Keywords: Generalized linear models; Incomplete data; Missing responses; Mixed models; Robust estimation;

    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. Geert Verbeke & Geert Molenberghs, 2005. "Longitudinal and incomplete clinical studies," Metron - International Journal of Statistics, Dipartimento di Statistica, ProbabilitĂ  e Statistiche Applicate - University of Rome, vol. 0(2), pages 143-176.
    2. J. G. Ibrahim & S. R. Lipsitz & M.-H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non-ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
    3. Dantan Etienne & Proust-Lima CĂ©cile & Letenneur Luc & Jacqmin-Gadda Helene, 2008. "Pattern Mixture Models and Latent Class Models for the Analysis of Multivariate Longitudinal Data with Informative Dropouts," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-26, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    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:spr:metrik:v:75:y:2012:i:7:p:913-938. 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: (Guenther Eichhorn) or (Christopher F Baum).

    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.