voiage.methods.network_meta_analysis.calculate_nma_evppi

calculate_nma_evppi

calculate_nma_evppi([positional or keyword] nma_data: NetworkMetaAnalysisData | dict[str, Any] = None, [positional or keyword] parameters_of_interest: list[str] = None, [positional or keyword] parameter_samples: dict[str, np.ndarray] = None, [positional or keyword] n_samples: int = 10000, [positional or keyword] willingness_to_pay: float | None = 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 partial perfect information for NMA.

Parameters

nma_data : NetworkMetaAnalysisData or dict[str, Any] Network meta-analysis inputs or a dictionary representation. parameters_of_interest : list[str] Parameter names whose uncertainty is being resolved. parameter_samples : dict[str, numpy.ndarray] PSA parameter samples. n_samples : int, default=10000 Number of PSA samples to use when extracting net benefits. willingness_to_pay : float, optional Willingness-to-pay threshold per unit. population : float, optional Population size for population scaling. time_horizon : float, optional Time horizon in years for population scaling. discount_rate : float, optional Annual discount rate used for population scaling.

Returns

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

Parameters:

• nma_data NetworkMetaAnalysisData | dict[str, Any]
• parameters_of_interest list[str]
• parameter_samples dict[str, np.ndarray]
• n_samples int (default: 10000)
• willingness_to_pay float | None (default: None)
• population float | None (default: None)
• time_horizon float | None (default: None)
• discount_rate float | None (default: None)

Returns: float