IDEAS home Printed from https://ideas.repec.org/c/boc/bocode/s459038.html
 

STIPW: Stata module to estimate inverse probability weighted parametric survival models with variance obtained via M-estimation

Author

Listed:
  • Micki Hill

    (University of Leicester)

Programming Language

Stata

Abstract

stipw performs an inverse probability weighted analysis on survival data. It begins by using logistic regression to model the treatment/exposure variable adjusting for the specified confounders. The propensity score is estimated and from this stabilised (a second logistic regression model is required in this case with no covariates) and unstabilised weights are calculated. A range of parametric models (modelled with streg or stpm2) can be fitted to the weighted data. The main advantage of stipw is that M-estimation is used to calculate the variance, which takes into account the uncertainty associated with the weight estimation.

Suggested Citation

  • Micki Hill, 2022. "STIPW: Stata module to estimate inverse probability weighted parametric survival models with variance obtained via M-estimation," Statistical Software Components S459038, Boston College Department of Economics.
    • Handle: RePEc:boc:bocode:s459038
    Note: This module should be installed from within Stata by typing "ssc install stipw". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/s/stipw.ado
    File Function: program code
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/s/stipw.sthlp
    File Function: help file
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/s/stipw_postestimation.sthlp
    File Function: help file
    Download Restriction: no
    ---><---

    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:boc:bocode:s459038. 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: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/debocus.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.