survML

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Tools for Flexible Survival Analysis Using Machine Learning

survML 최신버젼 다운로드

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# `survML`: Tools for Flexible Survival Analysis Using Machine Learning

The `survML` package contains a variety of functions for analyzing
survival data using machine learning. These include:

1.  *Global and local survival stacking*: Use off-the-shelf machine
    learning tools to estimate conditional survival functions.

2.  *Algorithm-agnostic variable importance*: Use debiased machine
    learning to estimate and make inference on variable importance for
    prediction of time-to-event outcomes. (1.2.0+)

3.  *Current-status isotonic regression*: Use isotonic regression to
    estimate the covariate-adjusted survival function of a time-to-event
    outcome under current status sampling. (1.2.0+)

See the package vignettes and function reference for more details.

## Installing `survML`

You can install a stable version of `survML` from CRAN using

``` r
install.packages("survML")
```

Alternatively, the development version of `survML` is available on
GitHub. You can install it using the `devtools` package as follows:

``` r
## install.packages("devtools") # run only if necessary
install_github(repo = "cwolock/survML")
```

## Integration with `CFsurvival`

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](https://github.com/cwolock/CFsurvival) of the
`CFsurvival` package, we have added `stackG()` from `survML` as an
option for estimating these nuisance parameters.

## Documentation

Full documentation can be found on the `survML` website at
<https://cwolock.github.io/survML/>.

## Bugs reports and feature requests

To submit a bug report or request a new feature, please submit a new
[GitHub Issue](https://github.com/cwolock/survML/issues).

## References

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.”](https://doi.org/10.1080/10618600.2024.2304070) *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.”](https://doi.org/10.1093/biomet/asae061) *Biometrika*
  (2025).

- **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,
  Stephen R. Cole, 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.”](https://doi.org/10.1097/EDE.0000000000001882)
  *Epidemiology* (2025).

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.](https://arxiv.org/abs/2107.13480)

## Citation

Citations for the various methods implemented in `survML` are given
below.

For conditional survival estimation using global survival stacking,
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}
    }

For variable importance, please cite the following:

@article{wolock2025assessing,
        author = {Wolock, Charles J and Gilbert, Peter B and Simon, Noah and Carone, Marco},
        title = {Assessing variable importance in survival analysis using machine learning},
        journal = {Biometrika},
        volume = {112},
        number = {2}
        pages = {asae061},
        year = {2025},
        doi = {10.1093/biomet/asae061},
        publisher={Oxford University Press}
    }

For covariate-adjusted survival curve estimation using current status
data, please cite the following:

@article{wolock2025investigating,
      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 Cole, Stephen R and Weil, Ana A and Chu, Helen Y and Carone, Marco},
      journal={Epidemiology},
      year={2025},
      volume = {36},
      number = {5},
      pages = {650--659},
      doi={10.1097/EDE.0000000000001882}
    }

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