ehrapy.plot.cox_ph_adjusted_curves

ehrapy.plot.cox_ph_adjusted_curves(edata, *, uns_key='cox_ph_adjusted_curves', groups=None, palette=None, show_ci=True, ci_alpha=0.15, line_width=2.0, width=650, height=450, title=None, xlabel='Time', ylabel='Adjusted Survival Probability', legend_position='top_right')

Survival curve plot to visualize CoxPH-adjusted survival probabilities stratified by a grouping variable.

The edata object must first be populated using cox_ph_adjusted_curves(). Mirrors the functionality of ggadjustedcurves() from the R survminer package. See also: Therneau, Crowson & Atkinson (2015), ‘Adjusted Survival Curves’: https://cran.r-project.org/web/packages/survival/vignettes/adjcurve.pdf

Parameters:

• edata (EHRData) – Data object containing the adjusted survival curves in uns after having run cox_ph_adjusted_curves().

• uns_key (str, default: 'cox_ph_adjusted_curves') – Key in .uns where cox_ph_adjusted_curves() stored its output. See argument key_added in cox_ph_adjusted_curves().

• groups (Sequence[str] | None, default: None) – Subset of group labels to plot. If None, all groups are plotted.

• palette (Sequence[str] | None, default: None) – List of hex or named colors, one per group.

• show_ci (bool, default: True) – Whether to draw confidence interval bands. Only has an effect when method=’average’ was used, as method=’conditional’ does not produce confidence intervals.

• ci_alpha (float, default: 0.15) – Transparency of the CI band fill between 0 and 1.

• line_width (float, default: 2.0) – Line width for the survival curves.

• width (int, default: 650) – Plot width in pixels.

• height (int, default: 450) – Plot height in pixels.

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

• xlabel (str, default: 'Time') – X-axis label.

• ylabel (str, default: 'Adjusted Survival Probability') – Y-axis label.

• legend_position (str, default: 'top_right') – Position of the legend.

Return type:

Overlay

Returns:

HoloViews Overlay object representing the adjusted survival curve plot.

Examples

>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.mimic_2()
>>> cph = ep.tl.cox_ph(
...     edata, "mort_day_censored", "censor_flg", formula="gender_num + afib_flg + day_icu_intime_num"
... )
>>> ep.tl.cox_ph_adjusted_curves(
...     edata,
...     cph,
...     strata="aline_flg",
...     duration_col="mort_day_censored",
...     event_col="censor_flg",
... )
>>> ep.pl.cox_ph_adjusted_curves(edata)