delta_method_log_mutinfo {Surrogate}

Variance of log-mutual information based on the delta method

Description

delta_method_log_mutinfo() computes the variance of the estimated log mutual information, given the unidentifiable parameters.

Usage

delta_method_log_mutinfo(
  fitted_model,
  copula_par_unid,
  copula_family2,
  rotation_par_unid,
  n_prec,
  mutinfo_estimator = NULL,
  composite,
  seed,
  eps = 0.001
)

Arguments

fitted_model

Returned value from fit_model_SurvSurv(). This object contains the estimated identifiable part of the joint distribution for the potential outcomes.

copula_par_unid

Parameter vector for the sequence of unidentifiable bivariate copulas that define the D-vine copula. The elements of copula_par correspond to (c_{23}, c_{13;2}, c_{24;3}, c_{14;23}).

copula_family2

Copula family of the other bivariate copulas. For the possible options, see loglik_copula_scale(). The elements of copula_family2 correspond to (c_{23}, c_{13;2}, c_{24;3}, c_{14;23}).

rotation_par_unid

Vector of rotation parameters for the sequence of unidentifiable bivariate copulas that define the D-vine copula. The elements of rotation_par correspond to (c_{23}, c_{13;2}, c_{24;3}, c_{14;23}).

n_prec

Number of Monte Carlo samples for the computation of the mutual information.

mutinfo_estimator

Function that estimates the mutual information between the first two arguments which are numeric vectors. Defaults to FNN::mutinfo() with default arguments. @param plot_deltas (logical) Plot the sampled individual treatment effects?

composite

(boolean) If composite is TRUE, then the surrogate endpoint is a composite of both a "pure" surrogate endpoint and the true endpoint, e.g., progression-free survival is the minimum of time-to-progression and time-to-death.

seed

Seed for Monte Carlo sampling. This seed does not affect the global environment.

eps

(numeric) Step size for finite difference in numeric differentiation

Details

This function should not be used. The ICA is computed through numerical methods with a considerable error. This error is negligible in individual estimates of the ICA; however, this error easily breaks the numeric differentiation because finite differences are inflated by this error.

Value

(numeric) Variance for the estimated ICA based on the delta method, holding the unidentifiable parameters fixed at the user supplied values.