IDEAS home Printed from https://ideas.repec.org/a/ctl/louvde/v89y2023i3p419-463.html
   My bibliography  Save this article

Survival analysis of longitudinal data: the case of English population aged 50 and over

Author

Listed:
  • Marjan QAZVINI

    (Department of Actuarial Mathematics and Statistics, School of Mathematical and Computer Sciences, Heriot-Watt University)

Abstract

This study considers data from 5 waves of the English Longitudinal Study of Ageing (ELSA). We aim to study the impact of demographic and self-rated health variables including disability and diseases on the survival of the population aged 50+. The disability variables that we consider are mobility impairment, difficulties in performing Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). One of the problems with the survey study is missing observations. This may happen due to different reasons, such as errors, nonresponse and temporary withdrawals. We address this problem by applying single and multiple imputation methods. We then fit a Generalized Linear model (GLM) and Generalized Linear Mixed model (GLMM) to our data and show that a GLMM performs better than a GLM in terms of information criteria. We also look at the predictability of our models in terms of the time-dependent receiver operating characteristic (ROC) and the area of ROC, i.e. AUC. We conclude that among the disability factors, IADL and among the diseases, cancer significantly affect the survival of the English population aged 50 and older.

Suggested Citation

  • Marjan QAZVINI, 2023. "Survival analysis of longitudinal data: the case of English population aged 50 and over," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 89(3), pages 419-463, September.
    • Handle: RePEc:ctl:louvde:v:89:y:2023:i:3:p:419-463
    DOI: 10.1017/dem.2023.3
    as

    Download full text from publisher

    File URL: https://doi.org/10.1017/dem.2023.3
    Download Restriction: no
    File URL: https://libkey.io/10.1017/dem.2023.3?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
    ---><---

    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:ctl:louvde:v:89:y:2023:i:3:p:419-463. 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: Sebastien SCHILLINGS (email available below). General contact details of provider: https://edirc.repec.org/data/iruclbe.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.