IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-52636-2_131.html
   My bibliography  Save this book chapter

Joint Analysis of Longitudinal and Time-to-Event Data

In: Principles and Practice of Clinical Trials

Author

Listed:
  • Zheng Lu

    (Clinical Pharmacology and Exploratory Development, Astellas Pharma)

  • Emmanuel Chigutsa

    (Pharmacometrics, Eli Lilly and Company)

  • Xiao Tong

    (Clinical Pharmacology, Biogen)

Abstract

The longitudinal and time-to-event data are two kinds of common data generated from various clinical trials across different therapeutic areas. Joint modeling is appropriate to estimate unbiased effect of covariates that are measured longitudinally and are related to the event on the time to an event and then could be applied to predict the time to an event. An underlying random effects structure links the survival and longitudinal sub-models and allows for individual-specific predictions. This chapter provided the basic backgrounds of longitudinal and time-to-event data commonly generated from clinical trials and derivations of the joint likelihood function to be maximized when jointly modeling longitudinal and time-to-event data. In addition to these theoretical backgrounds, different applications of joint modeling of longitudinal and time-to-event data across different therapeutic areas and individual dynamic prediction were also extensively discussed.

Suggested Citation

  • Zheng Lu & Emmanuel Chigutsa & Xiao Tong, 2022. "Joint Analysis of Longitudinal and Time-to-Event Data," Springer Books, in: Steven Piantadosi & Curtis L. Meinert (ed.), Principles and Practice of Clinical Trials, chapter 97, pages 1919-1935, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-52636-2_131
    DOI: 10.1007/978-3-319-52636-2_131
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    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:spr:sprchp:978-3-319-52636-2_131. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.