evsi_regression

voi.evsi_regression(
    outcomes,
    summaries,
    wtp,
    *,
    effect=None,
    method='spline',
    n_knots=5,
    degree=3,
    seed=None,
)

EVSI by nonparametric regression on simulated study summaries.

Parameters

Name	Type	Description	Default
outcomes	Outcomes	Outcomes from a probabilistic sensitivity analysis.	required
summaries	pd.DataFrame	Simulated study summaries, one row per iteration, aligned on the outcomes’ iteration index (see simulate_summaries).	required
wtp	float	Willingness to pay per unit of effect.	required
effect	str | None	Effect column (default: the primary effect).	None
method	str	Metamodel; see heormodel.voi.evppi.evppi.	'spline'
n_knots	int	Spline knot count.	5
degree	int	Spline degree.	3
seed	int | None	Subsample seed for the GP method.	None

Returns

Name	Type	Description
	float	The EVSI estimate, clipped at zero.

Example

import numpy as np, pandas as pd from heormodel.models import Outcomes from heormodel.voi import evsi_regression rng = np.random.default_rng(0) n = 4000 e_b = rng.normal(0.0, 1.0, n) out = Outcomes.from_wide( … pd.DataFrame({“A”: np.zeros(n), “B”: np.zeros(n)}), … pd.DataFrame({“A”: np.zeros(n), “B”: e_b})) s = pd.DataFrame({“xbar”: e_b + rng.normal(0, 0.1, n)}) 0.0 <= evsi_regression(out, s, wtp=1.0) <= 0.45 True