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.
dcurves is a Python package for performing Decision Curve Analysis (DCA). It evaluates and compares prediction models for both binary and survival outcomes.
Main functions:
dca(): Performs Decision Curve Analysis, calculating net benefit and interventions avoidedplot_graphs(): Visualizes DCA resultsload_test_data(): Provides sample data for testing and examples
This tutorial will guide you through installing and using the dcurves package to perform Decision Curve Analysis (DCA) with sample cancer diagnosis data.
# Install dcurves for DCA
pip install dcurves
# Install other required packages
pip install pandas numpy statsmodels lifelines# Import Libraries
from dcurves import dca, plot_graphs, load_test_data
import pandas as pd
import numpy as np
# Load Package Simulation Data
df_binary = load_test_data.load_binary_df()
# Perform Decision Curve Analysis
df_dca = \
dca(
data=df_binary,
outcome='cancer',
modelnames=['famhistory'],
thresholds=np.arange(0, 0.36, 0.01),
)
# Standard DCA Plot
plot_graphs(
plot_df=df_dca,
graph_type='net_benefit',
y_limits=[-0.05, 0.2]
)
In-depth Tutorial and Explanations with Examples, Relevant Literature, and Forumn for Personalized Help
Visit https://www.decisioncurveanalysis.org
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.
- Shaun Porwal ([email protected])
- Rohan Singh ([email protected])