Surrogate (version 3.3.3)

sample_deltas_BinCont: Sample individual casual treatment effects from given D-vine copula model in binary continuous setting

Description

Sample individual casual treatment effects from given D-vine copula model in binary continuous setting

Usage

sample_deltas_BinCont(
  copula_par,
  rotation_par,
  copula_family1,
  copula_family2 = copula_family1,
  n,
  q_S0 = NULL,
  q_S1 = NULL,
  q_T0 = NULL,
  q_T1 = NULL,
  marginal_sp_rho = TRUE,
  setting = "BinCont",
  composite = FALSE,
  plot_deltas = FALSE,
  restr_time = +Inf
)

Value

A list with two elements:

• Delta_dataframe: a dataframe containing the sampled individual causal treatment effects

• marginal_sp_rho_matrix: a matrix containing the marginal pairwise Spearman's rho parameters estimated from the sample. If marginal_sp_rho = FALSE, this matrix is not computed and NULL is returned for this element of the list.

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

Number of samples to be taken from the D-vine copula.

q_S0

Quantile function for the distribution of $S_0$.

q_S1

Quantile function for the distribution of $S_1$.

q_T0

Quantile function for the distribution of $T_0$. This should be NULL if $T_0$ is binary.

q_T1

Quantile function for the distribution of $T_1$. This should be NULL if $T_1$ is binary.

marginal_sp_rho

(boolean) Compute the sample Spearman correlation matrix? Defaults to TRUE.

setting

Should be one of the following two:

• "BinCont": for when $S$ is continuous and $T$ is binary.

• "SurvSurv": for when both $S$ and $T$ are time-to-event variables.

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.

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).