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voiage.methods.basic.evpi

evpi

Section titled “evpi”
evpi([positional or keyword] nb_array: np.ndarray | ValueArray = None, [positional or keyword] population: float | None = None, [positional or keyword] time_horizon: float | None = None, [positional or keyword] discount_rate: float | None = None) -> float

Calculate expected value of perfect information.

Parameters

Section titled “Parameters”

nb_array : numpy.ndarray or ValueArray Net-benefit samples with shape (n_samples, n_strategies). population : float, optional Population size for population-scaled EVPI. time_horizon : float, optional Time horizon in years for population scaling. discount_rate : float, optional Annual discount rate used for population scaling.

Returns

Section titled “Returns”

float EVPI on a per-decision basis unless population scaling is requested.

Notes

Section titled “Notes”

EVPI is the gap between the expected value of the strategy that would be chosen with perfect information and the value of the strategy chosen under current information:

.. math::

\mathrm{EVPI} = E\left[\max_i NB_i(\theta)\right] - \max_i E[NB_i(\theta)].

References

Section titled “References”

Briggs, A. H., Claxton, K., & Sculpher, M. J. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press. Claxton, K. (1999). The irrelevance of inference: A decision-making approach to the stochastic evaluation of health care technologies.

Examples

Section titled “Examples”

>>> import numpy as np >>> from voiage.methods.basic import evpi >>> nb = np.array([[10.0, 12.0], [11.0, 9.0], [13.0, 14.0]]) >>> round(evpi(nb), 6) 0.666667

Parameters:

  • nb_array np.ndarray | ValueArray
  • population float | None (default: None)
  • time_horizon float | None (default: None)
  • discount_rate float | None (default: None)

Returns: float