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

Micki Hill ()
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Micki Hill: University of Leicester

Statistical Software Components from Boston College Department of Economics

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.

Language: Stata
Requires: Stata version 15.1, dm79 from STB-56, stpm2 and rcsgen from SSC (q.v.)
Keywords: survival model; inverse probability weighting; M-estimation (search for similar items in EconPapers)
Date: 2022-01-18
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.
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Downloads: (external link)
http://fmwww.bc.edu/repec/bocode/s/stipw.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/s/stipw.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/s/stipw_postestimation.sthlp help file (text/plain)

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