Comments (1)
Unfortunately the implemented AIPW is not ready for survival data. Briefly, AIPW works by combining the IPW with the predicted outcomes values (from g-computation). The formula that combines the two leads to all the wonderful AIPW properties. So right now, AIPTW
takes exposures and outcomes as input. There is no option to keep track of time, so it won't work as expected for survival data.
While not yet implemented, Longitudinal AIPW and Longitudinal TMLE (this is in the works but I have't worked on it recently) could technically be applied to this problem. You would divide the survival data into equally-sized checks of time, then use those algorithms. This is common practice for time-varying confounding. But like I said, these are not yet implemented but are on the short list.
However, you can use SurvivalGFormula
to compare with your IPW results. HERE are some reference documentations. While AIPW doesn't exist a comparison between the IPW and g-formula results would help to catch obvious instances of model misspecification.
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Related Issues (20)
- IPTW handle PerfectSeparationErrors in the marginal structural model better
- Dual treatments
- ValueError better pytest strategy
- Package compatibility? HOT 2
- Update documentation (and possibly re-organize) HOT 2
- MonteCarloGFormula
- Add Odds Ratio and other estimands for AIPTW and TMLE
- Addition of meta-analysis tools
- add p-value column in a forrest plot/ effectmeasureplot HOT 2
- Enhancement in graphics.py to change odds text size HOT 1
- Saving DAGs programatically HOT 11
- sklearn dependancy in setup.py should be scikit-learn HOT 1
- AIPW formula equivalent to what's in the literature? HOT 2
- Perfect separation error for using `SingleCrossfitTMLE` HOT 2
- Superlearn check weights HOT 2
- SingleCrossFit `invalid value encountered in log` HOT 8
- Unable to install latest 0.9.0 version through pip HOT 7
- Risk Ratio Summary HOT 1
- The default regression argument of zepid.base.interaction_contrast_ratio differs from the description in the documentation. HOT 4
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