dcurves 0.0.3

Python package for Andrew Vickers' Decision Curve Analysis method to evaluate prediction models and diagnostic tests

dcurves

Dcurves is a Python library for Andrew Vickers' Decision Curve Analysis method to evaluate prediction models and diagnostic tests.

Installation

Use the package manager pip to install dcurves.

pip install dcurves

Usage

import dcurves

# load provided simulation dataset(s)

# load (1) binary endpoint data
df_binary = dcurves.load_test_data.load_binary_df()
# load (2) survival endpoint data
df_surv = dcurves.load_test_data.load_survival_df()
# load (3) case-control endpoint data
df_case_control = dcurves.load_test_data.load_case_control_df()
# load (4) 2nd binary endpoint data 
df_cancer_dx = dcurves.load_test_data.load_cancerdx_df()

# run decision curve analysis on dataset of choice

# (1) binary endpoint data
binary_output_df = dcurves.dca(
        data = df_binary,
        outcome = 'cancer',
        predictors = ['cancerpredmarker', 'marker'],
        thresh_vals = [0.01, 1.0, 0.01],
        probabilities = [False, True]
)

# (2) survival endpoint data

survival_output_df = dcurves.dca(
    data = df_surv,
    outcome = 'cancer',
    predictors = ['cancerpredmarker'],
    thresh_vals = [0.01, 1.0, 0.01],
    probabilities = [False],
    time = 1,
    time_to_outcome_col = 'ttcancer'
)

# (4) 2nd binary endpoint data

dan_test_output_df = dcurves.dca(
    data = df_cancer_dx,
    outcome = 'cancer',
    predictors = ['famhistory'],
    thresh_vals = [0.01, 1.0, 0.01],
    probabilities = [False]
)

# plot DCA results for binary endpoint (ideally in a jupyter/other .ipynb notebook

dcurves.plot_net_benefit_graphs(binary_output_df, y_limits=[-0.05, 0.2], color_names=['lightgreen', 'blue', 'red'])

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