voiage.methods.adaptive.adaptive_evsi

adaptive_evsi

adaptive_evsi([positional or keyword] adaptive_trial_simulator: AdaptiveTrialEconomicSim = None, [positional or keyword] psa_prior: PSASample = None, [positional or keyword] base_trial_design: TrialDesign = None, [positional or keyword] adaptive_rules: dict[str, object] = 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 = 10, [positional or keyword] n_inner_loops: int = 50, [variadic keyword] kwargs: object = {}) -> float

Calculate expected value of sample information for an adaptive trial.

Parameters

adaptive_trial_simulator : callable Simulator that produces net-benefit samples for an adaptive design. psa_prior : ParameterSet Prior PSA sample representing current uncertainty. base_trial_design : TrialDesign Initial trial design before adaptation. adaptive_rules : dict[str, object] Adaptive rules such as interim timings and stopping criteria. 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=10 Number of outer Monte Carlo draws. n_inner_loops : int, default=50 Number of inner Monte Carlo draws. **kwargs : object Additional simulator-specific arguments.

Returns

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

Parameters:

• adaptive_trial_simulator AdaptiveTrialEconomicSim
• psa_prior PSASample
• base_trial_design TrialDesign
• adaptive_rules dict[str, object]
• population float | None (default: None)
• discount_rate float | None (default: None)
• time_horizon float | None (default: None)
• n_outer_loops int (default: 10)
• n_inner_loops int (default: 50)
• kwargs object (default: {})

Returns: float