dcurves 1.0.5

A Python package for Decision Curve Analysis to evaluate prediction models, molecular markers, and diagnostic tests. For RELEASE NOTES, check RELEASE.md here: https://github.com/MSKCC-Epi-Bio/dcurves/RELEASE.md

dcurves

Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes, but often require collection of additional information that may be cumbersome to apply to models that yield continuous results. Decision Curve Analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requiring only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. The dca function performs decision curve analysis for binary and survival outcomes. Review the DCA tutorial (towards the bottom) for a detailed walk-through of various applications. Also, see www.decisioncurveanalysis.org for more information.

Installation

Use the package manager pip to install dcurves

While this is the quick-and-dirty method to install a package such as dcurves into your local environment, you should use a virtual environment and make sure your dependencies are compatible while using dcurves

pip install dcurves

Import dcurves and numpy

from dcurves import dca, plot_graphs, load_test_data
import numpy as np

Usage - Binary Outcomes

from dcurves import dca, plot_graphs, load_test_data
import numpy as np

dca_results = \
    dca(
        data=load_test_data.load_binary_df(),
        outcome='cancer',
        modelnames=['famhistory'],
        thresholds=np.arange(0,0.46,0.01)
    )

plot_graphs(
    plot_df=dca_results,
    graph_type='net_benefit',
    y_limits=[-0.05, 0.15],
    color_names=['blue', 'red', 'green']
)

Usage - Survival Outcomes

from dcurves import dca, plot_graphs, load_test_data
import numpy as np

dca_results = \
    dca(
        data=load_test_data.load_survival_df(),
        outcome='cancer',
        modelnames=['famhistory', 'marker', 'cancerpredmarker'],
        models_to_prob=['marker'],
        thresholds=np.arange(0,0.46,0.01),
        time_to_outcome_col='ttcancer',
        time=1
    )

plot_graphs(
    plot_df=dca_results,
    graph_type='net_benefit',
    y_limits=[-0.025, 0.175],
    color_names=['blue', 'red', 'green', 'purple', 'black']
)

In-depth tutorial and explanations:

https://www.danieldsjoberg.com/dca-tutorial/dca-tutorial-python.html

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change

Please make sure to update tests as appropriate

License

Apache 2.0

Note

setup.py is deprecated now that dependencies are managed by poetry package manager