pydts 0.9.6

Discrete time survival analysis with competing risks

Discrete Time Survival Analysis

A Python package for discrete-time survival data analysis with competing risks.

Tomer Meir, Rom Gutman, Malka Gorfine 2022

Documentation

Installation

pip install pydts

Quick Start

from pydts.fitters import TwoStagesFitter
from pydts.examples_utils.generate_simulations_data import generate_quick_start_df

patients_df = generate_quick_start_df(n_patients=10000, n_cov=5, d_times=14, j_events=2, pid_col='pid', seed=0)

fitter = TwoStagesFitter()
fitter.fit(df=patients_df.drop(['C', 'T'], axis=1))
fitter.print_summary()

Examples

1. Usage Example
2. Hospital Length of Stay Simulation Example

Citations

If you found PyDTS software useful to your research, please cite the papers:

@article{Meir_PyDTS_2022,
    author = {Meir, Tomer and Gutman, Rom, and Gorfine, Malka},
    doi = {10.48550/arXiv.2204.05731},
    title = {{PyDTS: A Python Package for Discrete Time Survival Analysis with Competing Risks}},
    url = {https://arxiv.org/abs/2204.05731},
    year = {2022}
}

@article{Meir_Gorfine_DTSP_2023,
    author = {Meir, Tomer and Gorfine, Malka},
    doi = {10.48550/arXiv.2303.01186},
    title = {{Discrete-time Competing-Risks Regression with or without Penalization}},
    url = {https://arxiv.org/abs/2303.01186},
    year = {2023}
}

and please consider starring the project on GitHub

How to Contribute

1. Open Github issues to suggest new features or to report bugs\errors
2. Contact Tomer or Rom if you want to add a usage example to the documentation
3. If you want to become a developer (thank you, we appreciate it!) - please contact Tomer or Rom for developers' on-boarding

Tomer Meir: [e-mail], Rom Gutman: [e-mail]