voiage.methods.observational.voi_observational

voi_observational

voi_observational([positional or keyword] obs_study_modeler: ObservationalStudyModeler | None = None, [positional or keyword] psa_prior: PSASample | None = None, [positional or keyword] observational_study_design: dict[str, object] | None = None, [positional or keyword] bias_models: dict[str, object] | None = 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, [variadic keyword] kwargs: object = {}) -> float

Calculate the value of information for an observational study.

Parameters

obs_study_modeler : callable, optional Modeler that maps PSA samples and study assumptions to net-benefit samples. psa_prior : ParameterSet Prior PSA samples representing current uncertainty. observational_study_design : dict[str, object] Study design specification. bias_models : dict[str, object] Bias-model specification. 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. **kwargs : object Additional modeler-specific options.

Returns

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

Parameters:

• obs_study_modeler ObservationalStudyModeler | None (default: None)
• psa_prior PSASample | None (default: None)
• observational_study_design dict[str, object] | None (default: None)
• bias_models dict[str, object] | None (default: None)
• population float | None (default: None)
• discount_rate float | None (default: None)
• time_horizon float | None (default: None)
• n_outer_loops int (default: 20)
• kwargs object (default: {})

Returns: float