pymsm 0.1.4

Multstate modeling in Python

Multistate competing risk models in Python.
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Hagai Rossman, Ayya Keshet, Malka Gorfine 2022

PyMSM is a Python implementation of Competing Risks and Multistate models for survival data.

Features include:

• Fit a Multistate model based on survival analysis models.
• Deals with right censoring, competing events, recurrent events, left truncation, and time-dependent covariates.
• Run Monte-carlo simulations for paths emitted by the trained model and extract various summary statistics and plots.
• Configure a pre-defined simulation model and run simulations.
• Modularity and compatibility for different time-to-event models such as Survival Forests or other custom models.

Installation

pip install pymsm

Quick example

# Load data
from pymsm.datasets import prep_rotterdam
dataset, states_labels = prep_rotterdam()

# Define terminal states
terminal_states = [3]

#Init MultistateModel
from pymsm.multi_state_competing_risks_model import MultiStateModel, default_update_covariates_function

multi_state_model = MultiStateModel(
    dataset,
    terminal_states,
    default_update_covariates_function)

# Fit to data
multi_state_model.fit()

# Run Monte-carlo simulation
all_mcs = multi_state_model.run_monte_carlo_simulation(
              sample_covariates = dataset[0].covariates.values,
              origin_state = 1,
              current_time = 0,
              max_transitions = 2,
              n_random_samples = 100)

Full examples

1. Rotterdam Illness-death example
2. EBMT multistate example
3. COVID hospitalizations multistate example

Citation

If you found this library useful in academic research, please cite:

@software{Rossman_PyMSM_Multistate_modeling_2022,
    author = {Rossman, Hagai and Keshet, Ayya and Gorfine, Malka},
    doi = {https://doi.org/10.5281/zenodo.6300873},
    license = {MIT},
    month = {2},
    title = {{PyMSM, Multistate modeling in Python}},
    url = {https://github.com/hrossman/pymsm},
    year = {2022}
}

Also consider starring the project on GitHub

This project is based on methods first introduced by the authors of Roimi et. al. 2021.
Original R code by Jonathan Somer, Asaf Ben Arie, Rom Gutman, Uri Shalit & Malka Gorfine available here. Also see Rossman & Meir et. al. 2021 for an application of this model.