compute_ICA_SurvSurv: Compute Individual Causal Association for a given D-vine...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Compute Individual Causal Association for a given D-vine copula model in the Survival-Survival Setting

Description

The compute_ICA_SurvSurv() function computes the individual causal association (and associated quantities) for a fully identified D-vine copula model in the survival-survival setting.

Usage

compute_ICA_SurvSurv(
  copula_par,
  rotation_par,
  copula_family1,
  copula_family2,
  n_prec,
  q_S0,
  q_T0,
  q_S1,
  q_T1,
  composite,
  marginal_sp_rho = TRUE,
  seed = 1,
  mutinfo_estimator = NULL,
  plot_deltas = FALSE,
  restr_time = +Inf
)

Arguments

copula_par	Parameter vector for the sequence of bivariate copulas that define the D-vine copula. The elements of copula_par correspond to (c_{12}, c_{23}, c_{34}, c_{13;2}, c_{24;3}, c_{14;23}).
rotation_par	Vector of rotation parameters for the sequence of bivariate copulas that define the D-vine copula. The elements of rotation_par correspond to (c_{12}, c_{23}, c_{34}, c_{13;2}, c_{24;3}, c_{14;23}).
copula_family1	Copula family of c_{12} and c_{34}. For the possible options, see loglik_copula_scale(). The elements of copula_family correspond to (c_{12}, c_{34}).
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}).
n_prec	Number of Monte Carlo samples for the computation of the mutual information.
q_S0	Quantile function for the distribution of S_0.
q_T0	Quantile function for the distribution of T_0.
q_S1	Quantile function for the distribution of S_1.
q_T1	Quantile function for the distribution of T_1.
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.
marginal_sp_rho	(boolean) Compute the sample Spearman correlation matrix? Defaults to TRUE.
seed	Seed for Monte Carlo sampling. This seed does not affect the global environment.
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?
plot_deltas	Plot the sampled individual causal effects? Defaults to FALSE.
restr_time	Restriction time for the potential outcomes. Defaults to +Inf which means no restriction. Otherwise, the sampled potential outcomes are replace by pmin(S0, restr_time) (and similarly for the other potential outcomes).

Value

(numeric) A Named vector with the following elements:

• ICA

• Spearman's rho, \rho_s (\Delta S, \Delta T) (if asked)

• Marginal association parameters in terms of Spearman's rho (if asked):

  \rho_{s}(T_0, S_0), \rho_{s}(T_0, S_1), \rho_{s}(T_0, T_1), \rho_{s}(S_0, S_1), \rho_{s}(S_0, T_1), \rho_{s}(S_1, T_1)

• Survival classification proportions (if asked):

  \pi_{harmed}, \pi_{protected}, \pi_{always}, \pi_{never}

Surrogate documentation built on June 22, 2024, 9:16 a.m.