as_outcomes

run.as_outcomes(
    source,
    *,
    intervention='intervention',
    iteration='iteration',
    cost='cost',
    effect='qaly',
    comparator=None,
)

Normalise any costs/effects table into the standard outcome structure.

This is the bring-your-own-outputs entry point: feed a tidy table from any source directly into the analysis layer.

Parameters

Name	Type	Description	Default
source	Outcomes | pd.DataFrame | str | Path	An Outcomes (returned unchanged), a tidy long DataFrame, or a path to a CSV file of one.	required
intervention	str	Column holding the intervention label.	'intervention'
iteration	str	Column holding the iteration index.	'iteration'
cost	str	Column holding the cost per iteration.	'cost'
effect	str	Column holding the effect (QALYs by default).	'qaly'
comparator	str | None	Name of the reference intervention, or None. Ignored when source is already an Outcomes.	None

Example

import pandas as pd from heormodel.run import as_outcomes df = pd.DataFrame({“intervention”: [“A”, “B”], “iteration”: [0, 0], … “cost”: [1.0, 2.0], “qaly”: [0.5, 0.7]}) as_outcomes(df).interventions [‘A’, ‘B’]