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voiage.methods.heterogeneity.value_of_heterogeneity

value_of_heterogeneity

Section titled “value_of_heterogeneity”
value_of_heterogeneity([positional or keyword] value_array: ValueArray = None, [positional or keyword] subgroups: np.ndarray | list[Any] = None, [positional or keyword] strategy_names: list[str] | None = None, [positional or keyword] n_bins: int | None = None) -> HeterogeneityResult

Calculate the value of tailoring decisions to subgroups.

Parameters

Section titled “Parameters”

value_array : ValueArray 2D net-benefit samples with shape (n_samples, n_strategies). subgroups : numpy.ndarray or list[Any] Subgroup label for each sample. strategy_names : list[str], optional Optional strategy labels. n_bins : int, optional Number of quantile bins to use when subgroups is numeric.

Returns

Section titled “Returns”

HeterogeneityResult Result containing subgroup-specific and overall expected net benefits.

Notes

Section titled “Notes”

The reported value is the gain from allowing different optimal strategies in different subgroups rather than applying one strategy to everyone:

.. math::

\mathrm{VOH} = \max\left(0,\sum_g w_g \max_d E[NB_{d,g}] - \max_d E[NB_d]\right).

References

Section titled “References”

Fenwick, E., Claxton, K., & Sculpher, M. (2001). Representing uncertainty: the cost-effectiveness acceptability curve, the cost-effectiveness acceptability frontier, and the value of heterogeneity. O’Cathain, A., et al. (2021). Equity and subgroup value in economic evaluation.

Examples

Section titled “Examples”

>>> import numpy as np >>> from voiage.methods.heterogeneity import value_of_heterogeneity >>> from voiage.schema import ValueArray >>> values = np.array([ … [10.0, 8.0], … [11.0, 7.0], … [6.0, 12.0], … [5.0, 13.0], … ]) >>> result = value_of_heterogeneity( … ValueArray.from_numpy(values, [“A”, “B”]), … subgroups=[“low”, “low”, “high”, “high”], … ) >>> result.value >= 0.0 True

Parameters:

  • value_array ValueArray
  • subgroups np.ndarray | list[Any]
  • strategy_names list[str] | None (default: None)
  • n_bins int | None (default: None)

Returns: HeterogeneityResult