voiage.methods.network_nma.evsi_nma

evsi_nma

evsi_nma([positional or keyword] nma_model_evaluator: NMAEconomicModelEvaluator = None, [positional or keyword] psa_prior_nma: PSASample = None, [positional or keyword] trial_design_new_study: TrialDesign = None, [positional or keyword] population: float | None = None, [positional or keyword] discount_rate: float | None = None, [positional or keyword] time_horizon: float | None = None, [positional or keyword] n_outer_loops: int = 20, [positional or keyword] n_inner_loops: int = 100, [variadic keyword] kwargs: object = {}) -> float

Calculate EVSI for a proposed study in a network meta-analysis.

Parameters

nma_model_evaluator : callable Model evaluator that maps PSA samples to net-benefit samples. psa_prior_nma : ParameterSet Prior PSA samples for the NMA and economic model. trial_design_new_study : TrialDesign Design of the new study to add to the network. population : float, optional Population size for population scaling. discount_rate : float, optional Annual discount rate used for population scaling. time_horizon : float, optional Time horizon in years for population scaling. n_outer_loops : int, default=20 Number of outer Monte Carlo draws. n_inner_loops : int, default=100 Number of inner Monte Carlo draws. **kwargs : object Additional model-evaluation options.

Returns

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

Parameters:

• nma_model_evaluator NMAEconomicModelEvaluator
• psa_prior_nma PSASample
• trial_design_new_study TrialDesign
• population float | None (default: None)
• discount_rate float | None (default: None)
• time_horizon float | None (default: None)
• n_outer_loops int (default: 20)
• n_inner_loops int (default: 100)
• kwargs object (default: {})

Returns: float