A Python package for discrete-time survival data analysis with competing risks.
Tomer Meir, Rom Gutman, Malka Gorfine 2022
pip install pydtsfrom 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()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
- Open Github issues to suggest new features or to report bugs\errors
- Contact Tomer or Rom if you want to add a usage example to the documentation
- If you want to become a developer (thank you, we appreciate it!) - please contact Tomer or Rom for developers' on-boarding
Tomer Meir: tomer1812@gmail.com, Rom Gutman: rom.gutman1@gmail.com
