IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v72y2016i3p917-925.html
   My bibliography  Save this article

A class of joint models for multivariate longitudinal measurements and a binary event

Author

Listed:
  • Sungduk Kim
  • Paul S. Albert

Abstract

type="main" xml:lang="en"> Predicting binary events such as newborns with large birthweight is important for obstetricians in their attempt to reduce both maternal and fetal morbidity and mortality. Such predictions have been a challenge in obstetric practice, where longitudinal ultrasound measurements taken at multiple gestational times during pregnancy may be useful for predicting various poor pregnancy outcomes. The focus of this article is on developing a flexible class of joint models for the multivariate longitudinal ultrasound measurements that can be used for predicting a binary event at birth. A skewed multivariate random effects model is proposed for the ultrasound measurements, and the skewed generalized t-link is assumed for the link function relating the binary event and the underlying longitudinal processes. We consider a shared random effect to link the two processes together. Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. Several variations of the proposed model are considered and compared via the deviance information criterion, the logarithm of pseudomarginal likelihood, and with a training-test set prediction paradigm. The proposed methodology is illustrated with data from the NICHD Successive Small-for-Gestational-Age Births study, a large prospective fetal growth cohort conducted in Norway and Sweden.

Suggested Citation

  • Sungduk Kim & Paul S. Albert, 2016. "A class of joint models for multivariate longitudinal measurements and a binary event," Biometrics, The International Biometric Society, vol. 72(3), pages 917-925, September.
  • Handle: RePEc:bla:biomet:v:72:y:2016:i:3:p:917-925
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Weiji Su & Xia Wang & Rhonda D. Szczesniak, 2021. "Flexible link functions in a joint hierarchical Gaussian process model," Biometrics, The International Biometric Society, vol. 77(2), pages 754-764, June.
    2. Khurshid Alam & Arnab Maity & Sanjoy K. Sinha & Dimitris Rizopoulos & Abdus Sattar, 2021. "Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 64-90, January.

    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:biomet:v:72:y:2016:i:3:p:917-925. 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: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.