IDEAS home Printed from https://ideas.repec.org/a/hin/jnljps/1019303.html
   My bibliography  Save this article

An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout

Author

Listed:
  • Amal Almohisen
  • Robin Henderson
  • Arwa M. Alshingiti

Abstract

In any longitudinal study, a dropout before the final timepoint can rarely be avoided. The chosen dropout model is commonly one of these types: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR), and Shared Parameter (SP). In this paper we estimate the parameters of the longitudinal model for simulated data and real data using the Linear Mixed Effect (LME) method. We investigate the consequences of misspecifying the missingness mechanism by deriving the so-called least false values. These are the values the parameter estimates converge to, when the assumptions may be wrong. The knowledge of the least false values allows us to conduct a sensitivity analysis, which is illustrated. This method provides an alternative to a local misspecification sensitivity procedure, which has been developed for likelihood-based analysis. We compare the results obtained by the method proposed with the results found by using the local misspecification method. We apply the local misspecification and least false methods to estimate the bias and sensitivity of parameter estimates for a clinical trial example.

Suggested Citation

  • Amal Almohisen & Robin Henderson & Arwa M. Alshingiti, 2019. "An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout," Journal of Probability and Statistics, Hindawi, vol. 2019, pages 1-10, July.
    • Handle: RePEc:hin:jnljps:1019303
    DOI: 10.1155/2019/1019303
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/JPS/2019/1019303.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/JPS/2019/1019303.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/1019303?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
    ---><---

    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:hin:jnljps:1019303. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.