ce_plane

cea.ce_plane(outcomes, *, comparator=None, effect=None)

Incremental cost and effect per iteration versus a comparator.

Parameters

Name	Type	Description	Default
outcomes	Outcomes	Outcomes from a probabilistic sensitivity analysis.	required
comparator	str | None	Reference intervention (default: the intervention flagged is_comparator=True at construction, via outcomes.comparator, or the first intervention if none was flagged).	None
effect	str | None	Effect column (default: the primary effect).	None

Returns

Name	Type	Description
	pd.DataFrame	Tidy DataFrame with columns intervention, iteration,
	pd.DataFrame	inc_cost and inc_effect for every non-comparator intervention,
	pd.DataFrame	ready to scatter on the cost-effectiveness plane.

Example

import pandas as pd from heormodel.models import Outcomes from heormodel.cea import ce_plane c = pd.DataFrame({“A”: [0.0], “B”: [10.0]}) e = pd.DataFrame({“A”: [0.0], “B”: [0.5]}) float(ce_plane(Outcomes.from_wide(c, e))[“inc_cost”][0]) 10.0