compute_ICA_SurvSurv: 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 = copula_family1,
  n_prec,
  minfo_prec,
  q_S0,
  q_T0,
  q_S1,
  q_T1,
  composite,
  marginal_sp_rho = TRUE,
  seed = 1
)

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): $$\pi_{harmed}, \pi_{protected}, \pi_{always}, \pi_{never}$$

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().
copula_family2: Copula family of the other bivariate copulas. For the possible options, see loglik_copula_scale().
n_prec: Number of Monte Carlo samples for the computation of the mutual information.
minfo_prec: Number of quasi Monte-Carlo samples for the numerical integration to obtain the mutual information. If this value is 0 (default), the mutual information is not computed and NA is returned for the mutual information and derived quantities.
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.