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).