voiage.methods.dominance.calculate_dominance

calculate_dominance

calculate_dominance([positional or keyword] costs: np.ndarray | list[float] = None, [positional or keyword] effects: np.ndarray | list[float] = None, [positional or keyword] strategy_names: list[str] | None = None) -> DominanceResult

Identify dominance classes and frontier ICERs.

Parameters

costs : numpy.ndarray or list[float] Strategy costs. effects : numpy.ndarray or list[float] Strategy effects. strategy_names : list[str], optional Optional strategy labels.

Returns

DominanceResult Dominance classification with frontier indices and ICERs.

Notes

Strong dominance removes any strategy that is at least as costly and no more effective than another strategy, with one dimension strictly worse. Extended dominance removes frontier strategies whose incremental cost-effectiveness ratios are not strictly increasing along the frontier.

References

Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes. Briggs, A. H., Claxton, K., & Sculpher, M. J. (2006). Decision Modelling for Health Economic Evaluation.

Examples

>>> import numpy as np >>> from voiage.methods.dominance import calculate_dominance >>> result = calculate_dominance( … costs=np.array([10.0, 12.0, 13.0]), … effects=np.array([1.0, 1.1, 1.3]), … strategy_names=[“A”, “B”, “C”], … ) >>> result.frontier_indices [0, 1, 2]

Parameters:

• costs np.ndarray | list[float]
• effects np.ndarray | list[float]
• strategy_names list[str] | None (default: None)

Returns: DominanceResult