Note: The current development version of survML
now has
functionality for estimating variable importance and for estimating a
covariate-adjusted survival curve under current status sampling, in
addition to the original survival stacking functionality that was
included in versions 1.1.0 and earlier. A new version on CRAN is
forthcoming.
The survML
package contains a variety of functions for analyzing
survival data using machine learning. These include:
-
Global and local survival stacking: Use off-the-shelf machine learning tools to estimate conditional survival functions.
-
Algorithm-agnostic variable importance: Use debiased machine learning to estimate and make inference on variable importance for prediction of time-to-event outcomes.
-
Current-status isotonic regression: Use isotonic regression to estimate the covariate-adjusted survival function of a time-to-event outcome under current status sampling.
See the package vignettes and function reference for more details.
You can install a stable version of survML
from CRAN using
install.packages("survML")
Alternatively, the development version of survML
is available on
GitHub. You can install it using the devtools
package as follows:
## install.packages("devtools") # run only if necessary
install_github(repo = "cwolock/survML")
The CFsurvival
package can be used to estimate a covariate-adjusted
counterfactual survival curve from observational data. This approach
requires estimating the conditional event and censoring distributions.
In this fork of the
CFsurvival
package, we have added stackG()
from survML
as an
option for estimating these nuisance parameters.
Full documentation can be found on the survML
website at
https://cwolock.github.io/survML/.
To submit a bug report or request a new feature, please submit a new GitHub Issue.
For details of the methods implemented in this package, please see the following papers:
-
Global survival stacking: Charles J. Wolock, Peter B. Gilbert, Noah Simon and Marco Carone. “A framework for leveraging machine learning tools to estimate personalized survival curves.” Journal of Computational and Graphical Statistics (2024).
-
Survival variable importance: Charles J. Wolock, Peter B. Gilbert, Noah Simon and Marco Carone. “Assessing variable importance in survival analysis using machine learning.” In press, Biometrika (2024).
-
Covariate-adjusted survival curves from current status data: Charles J. Wolock, Susan Jacob, Julia C. Bennett, Anna Elias-Warren, Jessica O’Hanlon, Avi Kenny, Nicholas P. Jewell, Andrea Rotnitzky, Ana A. Weil, Helen Y. Chu and Marco Carone. “Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome.” arXiv:2407.04214.
Local survival stacking is described in:
-
Eric C. Polley and Mark J. van der Laan. “Super Learning for Right-Censored Data” in Targeted Learning (2011).
-
Erin Craig, Chenyang Zhong, and Robert Tibshirani. “Survival stacking: casting survival analysis as a classification problem.” arXiv:2107.13480.
After using the survML
package for conditional survival estimation,
please cite the following:
@article{wolock2024framework,
title={A framework for leveraging machine learning tools to estimate personalized survival curves},
author={Wolock, Charles J and Gilbert, Peter B and Simon, Noah and Carone, Marco},
journal={Journal of Computational and Graphical Statistics},
year={2024},
volume = {33},
number = {3},
pages = {1098--1108},
publisher={Taylor \& Francis},
doi={10.1080/10618600.2024.2304070}
}
After using the variable importance functions, please cite the following:
@article{wolock2023assessing,
title={Assessing variable importance in survival analysis using machine learning},
author={Wolock, Charles J and Gilbert, Peter B and Simon, Noah and Carone, Marco},
journal={arXiv preprint arXiv:2311.12726},
year={2023}
}
After using the functionality for current status data, please cite the following:
@article{wolock2024investigating,
title={Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome},
author={Wolock, Charles J and Jacob, Susan and Bennett, Julia C and Elias-Warren, Anna and O'Hanlon, Jessica and Kenny, Avi and Jewell, Nicholas P and Rotnitzky, Andrea and Weil, Ana A and Chu, Helen Y and Carone, Marco},
journal={arXiv preprint arXiv:2407.04214},
year={2024}
}