frontier

cea.frontier

Incremental cost-effectiveness analysis on the efficiency frontier.

Implements the standard decision-analytic algorithm: order interventions by cost, remove strongly dominated interventions (more costly and no more effective than another), then iteratively remove extendedly dominated interventions (whose ICER exceeds that of the next more effective intervention) until ICERs increase monotonically along the frontier.

Functions

Name	Description
frontier	Interventions on the cost-effectiveness efficiency frontier, cheapest first.
icer_table	Full incremental analysis: dominance, extended dominance, and ICERs.

frontier

cea.frontier.frontier(source, *, effect=None)

Interventions on the cost-effectiveness efficiency frontier, cheapest first.

Example

import pandas as pd from heormodel.cea import frontier means = pd.DataFrame( … {“cost”: [0.0, 10.0, 5.0], “effect”: [0.0, 1.0, -1.0]}, … index=[“A”, “B”, “C”], … ) frontier(means) [‘A’, ‘B’]

icer_table

cea.frontier.icer_table(source, *, effect=None)

Full incremental analysis: dominance, extended dominance, and ICERs.

Parameters

Name	Type	Description	Default
source	Outcomes | pd.DataFrame	probabilistic Outcomes (means are taken per intervention) or a per-intervention mean table indexed by intervention with columns cost and the effect column.	required
effect	str | None	Effect column name (defaults to the outcomes’ primary effect, or "effect" for plain tables).	None

Returns

Name	Type	Description
	pd.DataFrame	DataFrame indexed by intervention, sorted by cost, with columns
	pd.DataFrame	cost, effect, inc_cost, inc_effect, icer and
	pd.DataFrame	status ("ND" on the frontier, "D" strongly dominated,
	pd.DataFrame	"ED" extendedly dominated). ICERs are computed between adjacent
	pd.DataFrame	frontier interventions; the cheapest frontier intervention has no ICER.

Example

import pandas as pd from heormodel.cea import icer_table means = pd.DataFrame( … {“cost”: [0.0, 100.0, 400.0], “effect”: [0.0, 0.5, 1.0]}, … index=[“A”, “B”, “D”], … ) t = icer_table(means) float(t.loc[“D”, “icer”]) 600.0