On the website, create quick tutorials demonstrating each of the implemented estimators, descriptions of how they work, and why you might want to use them. Might be more digestible than the current docs (also better justify why to choose one over the other)
pzivich
commented
5 years ago
On the website, create quick tutorials demonstrating each of the implemented estimators, descriptions of how they work, and why you might want to use them. Might be more digestible than the current docs (also better justify why to choose one over the other)
Reference to base on https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html https://github.com/CamDavidsonPilon/lifelines/blob/master/docs/jupyter_notebooks/Proportional%20hazard%20assumption.ipynb
TODO
[x] Basic measures
[x] splines
[x] IPTW: time-fixed treatment
[ ] IPTW: stochastic treatment
[ ] IPTW: time-varying treatment
[x] IPCW
[x] IPMW: single variable
[ ] IPMW: monotone
[ ] IPMW: nonmonotone (to add after implemented)
[x] G-formula: time-fixed binary treatment, binary outcome
[x] G-formula: time-fixed categorical treatment, binary outcome
[ ] G-formula: time-fixed continuous treatment, binary outcome (to add after implemented)
[x] G-formula: time-fixed binary treatment, continuous outcome
[x] G-formula: Monte Carlo
[x] G-formula: Iterative Conditional
[x] G-estimation of SNM
[x] AIPTW
[ ] AIPMW
[x] TMLE
[x] TMLE: stochastic treatment
[ ] LTMLE (to add after implemented)
[x] Quantitative bias analysis
[x] Functional form assessment
[x] Generalizability
[ ] Transportability (IPSW, g-transport, AIPSW)
[x] Monte Carlo g-formula by-hand (helps to explain underlying process)