survival curves adjusted by baseline covariates

[CamDavidsonPilon]

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3653612/#R5

[pzivich]

Related: https://www.ncbi.nlm.nih.gov/pubmed/15158046 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983650/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4310506/

Also related to reference 5 in your linked article, are there any plans to implement accelerated failure time models in lifelines?

[CamDavidsonPilon]

Also related to reference 5 in your linked article, are there any plans to implement accelerated failure time models in lifelines?

yup, it's a priority now.

Here's a quick roadmap:

1. Robust errors for cox models
2. AFT models
3. Clean up AalenAdditiveModel code
4. survival curves adjusted by baseline covariates

[pzivich]

Robust errors for cox models

Does the robust errors for cox models include adding weights to the time varying cox model? Ultimately, the marginal structural models I am interested in estimate risk difference/ratio, however clinicians do like hazard ratios

AFTmodels

Based on my Biostatistics class, AFT shouldn't be terrible to implement. Collett's "Modeling Survival Data in Medical Research" gives a pretty good description

survival curves adjusted by baseline covariates

What's the strategy for adjusting survival curves by baseline covariates? Inverse probability weights?

[CamDavidsonPilon]

For the weights in cox model, the robust errors are a prerequisite, so that needs to get done first. (Adding weights will be in that addition too)

For the survival curves, hernan in the paper I linked has a “recipe” I want to try.

[pzivich]

Wanted to follow-up with this since I recently reread the "Hazards of Hazard Ratios". The 'Hernan' approach is just a disguished version of the g-formula / standardization. I am happy to chat further about it sometime. Basically, it is a special case of the MonteCarloGFormula I have written