IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v67y2005i1p167-182.html
   My bibliography  Save this article

Asymptotic bias in the linear mixed effects model under non‐ignorable missing data mechanisms

Author

Listed:
  • Chandan Saha
  • Michael P. Jones

Abstract

Summary. In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non‐ignorable drop‐out, drop‐in and other missing data patterns. The asymptotic bias is large when the drop‐out subjects have only one or no observation, especially for slope‐related parameters of the linear mixed effects model. In the drop‐in case, intercept‐related parameter estimators show substantial asymptotic bias when subjects enter late in the study. Eight other missing data patterns are considered and these produce asymptotic biases of a variety of magnitudes.

Suggested Citation

  • Chandan Saha & Michael P. Jones, 2005. "Asymptotic bias in the linear mixed effects model under non‐ignorable missing data mechanisms," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 167-182, February.
    • Handle: RePEc:bla:jorssb:v:67:y:2005:i:1:p:167-182
    DOI: 10.1111/j.1467-9868.2005.00494.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9868.2005.00494.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9868.2005.00494.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Robert M. Elashoff & Gang Li & Ning Li, 2008. "A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types," Biometrics, The International Biometric Society, vol. 64(3), pages 762-771, September.
    2. Christos Thomadakis & Loukia Meligkotsidou & Nikos Pantazis & Giota Touloumi, 2019. "Longitudinal and time‐to‐drop‐out joint models can lead to seriously biased estimates when the drop‐out mechanism is at random," Biometrics, The International Biometric Society, vol. 75(1), pages 58-68, March.

    More about this item

    Statistics

    Access and download statistics

    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:bla:jorssb:v:67:y:2005:i:1:p:167-182. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.