format_icer_table

report.format_icer_table(source, *, effect=None, interval=0.95, digits=None)

Render the ICER table for reading, with rounded numbers and intervals.

Parameters

Name	Type	Description	Default
source	Outcomes | pd.DataFrame	Same input as heormodel.cea.icer_table: a probabilistic Outcomes or a per-intervention mean table.	required
effect	str | None	Effect column name, passed through to icer_table.	None
interval	float | None	Central probability for the uncertainty interval, passed through to icer_table. With a probabilistic Outcomes of more than one iteration, each estimate is written point (low, high); otherwise only the point estimate is shown.	0.95
digits	int | Mapping[str, int] | None	Decimal places. An integer applies to every measure; a mapping from measure name ("cost", "effect", "inc_cost", "inc_effect", "icer") sets them one at a time and falls back to the default for any measure left out. The default rounds costs and the ratio to whole units and effects to two decimals.	None

Returns

Name	Type	Description
	pd.DataFrame	DataFrame of strings indexed by intervention, sorted by cost, with
	pd.DataFrame	sentence-case columns Cost, Effect, Incremental cost,
	pd.DataFrame	Incremental effect, ICER and Status. Cells with no value (the
	pd.DataFrame	cheapest frontier intervention’s incremental columns, or any dominated
	pd.DataFrame	intervention’s ICER) are blank.

Example

import pandas as pd from heormodel.report import format_icer_table means = pd.DataFrame( … {“cost”: [0.0, 100.0, 400.0], “effect”: [0.0, 0.5, 1.0]}, … index=[“A”, “B”, “D”], … ) format_icer_table(means).loc[“D”, “ICER”] ‘600’