IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v103y2016i3p734-741..html
   My bibliography  Save this article

A goodness-of-fit test for structural nested mean models

Author

Listed:
  • S. Yang
  • J. J. Lok

Abstract

Coarse structural nested mean models are tools for estimating treatment effects from longitudinal observational data with time-dependent confounding. There is, however, no guidance on how to specify the treatment effect model, and model misspecification can lead to bias. We derive a goodness-of-fit test based on modified over-identification restrictions tests for evaluating a treatment effect model, and show that our test is doubly robust in the sense that, with a correct treatment effect model, the test has the correct Type I error if either the treatment initiation model or a nuisance regression outcome model is correctly specified. In a simulation study, we show that the test has correct Type I error and can detect model misspecification. We use the test to study how the timing of antiretroviral treatment initiation after HIV infection predicts the effect of one year of treatment in HIV-positive patients with acute and early infection.

Suggested Citation

  • S. Yang & J. J. Lok, 2016. "A goodness-of-fit test for structural nested mean models," Biometrika, Biometrika Trust, vol. 103(3), pages 734-741.
    • Handle: RePEc:oup:biomet:v:103:y:2016:i:3:p:734-741.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/asw031
    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. Yasuhiro Hagiwara & Tomohiro Shinozaki & Yutaka Matsuyama, 2020. "G‐estimation of structural nested restricted mean time lost models to estimate effects of time‐varying treatments on a failure time outcome," Biometrics, The International Biometric Society, vol. 76(3), pages 799-810, September.
    2. Shu Yang, 2022. "Semiparametric estimation of structural nested mean models with irregularly spaced longitudinal observations," Biometrics, The International Biometric Society, vol. 78(3), pages 937-949, September.

    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:oup:biomet:v:103:y:2016:i:3:p:734-741.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.