IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v53y2004i2p293-310.html
   My bibliography  Save this article

Non‐ignorable missing covariate data in survival analysis: a case‐study of an International Breast Cancer Study Group trial

Author

Listed:
  • Amy H. Herring
  • Joseph G. Ibrahim
  • Stuart R. Lipsitz

Abstract

Summary. Non‐ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality‐of‐life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality‐of‐life data are likely to be non‐ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non‐ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.

Suggested Citation

  • Amy H. Herring & Joseph G. Ibrahim & Stuart R. Lipsitz, 2004. "Non‐ignorable missing covariate data in survival analysis: a case‐study of an International Breast Cancer Study Group trial," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 293-310, April.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:2:p:293-310
    DOI: 10.1046/j.1467-9876.2003.05168.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1046/j.1467-9876.2003.05168.x
    Download Restriction: no
    File URL: https://libkey.io/10.1046/j.1467-9876.2003.05168.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. Soyoung Kim & Jae-Kwang Kim & Kwang Woo Ahn, 2022. "A calibrated Bayesian method for the stratified proportional hazards model with missing covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 169-193, April.
    2. S. Eftekhari Mahabadi & M. Ganjali, 2012. "An index of local sensitivity to non-ignorability for parametric survival models with potential non-random missing covariate: an application to the SEER cancer registry data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2327-2348, July.
    3. Chen, Ming-Hui & Ibrahim, Joseph G. & Shao, Qi-Man, 2009. "Maximum likelihood inference for the Cox regression model with applications to missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2018-2030, October.
    4. Francesco Lagona & Zhen Zhang, 2008. "A missing composite covariate in survival analysis: a case study of the Chinese Longitudinal Health and Longevity Survey," MPIDR Working Papers WP-2008-022, Max Planck Institute for Demographic Research, Rostock, Germany.

    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:jorssc:v:53:y:2004:i:2:p:293-310. 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.