masurvival 0.1.2

MASurvival: Missingness-Avoiding Survival Forest - An extension of scikit-survival with missingness-avoidance regularization for handling missing data in survival analysis

MASurvival (Missingness-Avoiding Survival Forest) is a Python module for survival analysis built on top of scikit-learn and based on scikit-survival. It extends scikit-survival with missingness-avoidance regularization, allowing survival analysis models to better handle missing data by penalizing splits on features with missing values.

Key Feature: The alpha parameter in RandomSurvivalForest penalizes splits on features with missing values, making the model more robust to missing data without requiring imputation.

About Survival Analysis

The objective in survival analysis (also referred to as time-to-event or reliability analysis) is to establish a connection between covariates and the time of an event. What makes survival analysis differ from traditional machine learning is the fact that parts of the training data can only be partially observed – they are censored.

For instance, in a clinical study, patients are often monitored for a particular time period, and events occurring in this particular period are recorded. If a patient experiences an event, the exact time of the event can be recorded – the patient’s record is uncensored. In contrast, right censored records refer to patients that remained event-free during the study period and it is unknown whether an event has or has not occurred after the study ended. Consequently, survival analysis demands for models that take this unique characteristic of such a dataset into account.

Requirements

• Python 3.10 or later

• ecos

• joblib

• numexpr

• numpy

• osqp

• pandas 2.0.0 or later

• scikit-learn 1.6 or 1.7

• scipy

• C/C++ compiler

Installation

Install MASurvival from PyPI:

pip install masurvival

Or install from source:

git clone https://github.com/anli66/masurvival
cd masurvival
pip install -e .

Examples

MASurvival provides a drop-in replacement for RandomSurvivalForest from scikit-survival with an additional alpha parameter for missingness-avoidance regularization:

from masurvival.ensemble import RandomSurvivalForest
from masurvival.metrics import concordance_index_censored

# Create model with missingness-avoidance regularization
rsf = RandomSurvivalForest(
    n_estimators=100,
    alpha=1.0,  # Penalty for splits on features with missing values
    random_state=42
)

rsf.fit(X_train, y_train)
predictions = rsf.predict(X_test)

Help and Support

Documentation

• Based on scikit-survival documentation: https://scikit-survival.readthedocs.io/

• For MASurvival-specific features, see the source code and examples.

References

MASurvival is based on scikit-survival. Please cite the original scikit-survival paper:

S. Pölsterl, “scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn,” Journal of Machine Learning Research, vol. 21, no. 212, pp. 1–6, 2020.

@article{sksurv,
  author  = {Sebastian P{\"o}lsterl},
  title   = {scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {212},
  pages   = {1-6},
  url     = {http://jmlr.org/papers/v21/20-729.html}
}

Note: MASurvival extends scikit-survival with missingness-avoidance regularization. If you use MASurvival in your research, please also cite this work appropriately.

@article{masurvival,
  author  = {Aneta Lisowska},
  title   = {MASurvival: Missingness-Avoiding Survival Forest},
  year    = {2025},
}