IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v67y2018i1p255-273.html
   My bibliography  Save this article

Pattern–mixture models with incomplete informative cluster size: application to a repeated pregnancy study

Author

Listed:
  • Ashok Chaurasia
  • Danping Liu
  • Paul S. Albert

Abstract

The incomplete informative cluster size problem is motivated by the National Institute of Child Health and Human Development consecutive pregnancies study, aiming to study the relationship between pregnancy outcomes and parity. These pregnancy outcomes are potentially associated with the number of births over a woman's lifetime, resulting in an incomplete informative cluster size (censored at the end of the study window). We develop a pattern–mixture model for informative cluster size by treating the lifetime number of births as a latent variable. We compare this approach with a simple alternative method that approximates the pattern–mixture model. We show that the latent variable approach has good statistical properties for estimating both the mean trajectory of birth weight and the proportion of gestational hypertension with increasing parity.

Suggested Citation

  • Ashok Chaurasia & Danping Liu & Paul S. Albert, 2018. "Pattern–mixture models with incomplete informative cluster size: application to a repeated pregnancy study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(1), pages 255-273, January.
    • Handle: RePEc:bla:jorssc:v:67:y:2018:i:1:p:255-273
    DOI: 10.1111/rssc.12226
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssc.12226
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssc.12226?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. A. A. Mitani & E. K. Kaye & K. P. Nelson, 2021. "Marginal analysis of multiple outcomes with informative cluster size," Biometrics, The International Biometric Society, vol. 77(1), pages 271-282, March.

    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:67:y:2018:i:1:p:255-273. 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-Blackwell Digital Licensing or Christopher F. Baum (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.