ehrapy.plot.cox_ph_forestplot

ehrapy.plot.cox_ph_forestplot(adata, *, uns_key='cox_ph', labels=None, fig_size=(10, 10), t_adjuster=0.1, ecolor='dimgray', size=3, marker='o', decimal=2, text_size=12, color='k', show=None, title=None)[source]

Generates a forest plot to visualize the coefficients and confidence intervals of a Cox Proportional Hazards model.

The adata object must first be populated using the cox_ph() function. This function stores the summary table of the CoxPHFitter in the .uns attribute of adata. The summary table is created when the model is fitted using the cox_ph() function. For more information on the CoxPHFitter, see the Lifelines documentation.

Inspired by zepid.graphics.EffectMeasurePlot (zEpid Package, https://pypi.org/project/zepid/).

Parameters:

  • adata (AnnData) – AnnData object containing the summary table from the CoxPHFitter. This is stored in the .uns attribute, after fitting the model using cox_ph().

  • uns_key (str, default: 'cox_ph') – Key in .uns where cox_ph() function stored the summary table. See argument uns_key in cox_ph().

  • labels (Iterable[str] | None, default: None) – List of labels for each coefficient, default uses the index of the summary ta

  • fig_size (tuple, default: (10, 10)) – Width, height in inches.

  • t_adjuster (float, default: 0.1) – Adjust the table to the right.

  • ecolor (str, default: 'dimgray') – Color of the error bars.

  • size (int, default: 3) – Size of the markers.

  • marker (str, default: 'o') – Marker style.

  • decimal (int, default: 2) – Number of decimal places to display.

  • text_size (int, default: 12) – Font size of the text.

  • color (str, default: 'k') – Color of the markers.

  • show (bool, default: None) – Show the plot, do not return figure and axis.

  • title (str | None, default: None) – Set the title of the plot.

Examples

>>> import ehrapy as ep
>>> adata = ep.dt.mimic_2(encoded=False)
>>> adata_subset = adata[:, ["mort_day_censored", "censor_flg", "gender_num", "afib_flg", "day_icu_intime_num"]]
>>> coxph = ep.tl.cox_ph(adata_subset, event_col="censor_flg", duration_col="mort_day_censored")
>>> ep.pl.cox_ph_forestplot(adata_subset)
../../_images/coxph_forestplot.png