summary_level_bootstrap_ICA: Bootstrap based on the multivariate normal sampling...
In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

View source: R/ITCI-Dvine-inference-utils.R

summary_level_bootstrap_ICA

R Documentation

Bootstrap based on the multivariate normal sampling distribution

Description

summary_level_bootstrap_ICA() performs a parametric type of bootstrap based on the estimated multivariate normal sampling distribution of the maximum likelihood estimator for the (observable) D-vine copula model parameters.

Usage

summary_level_bootstrap_ICA(
  fitted_model,
  copula_par_unid,
  copula_family2,
  rotation_par_unid,
  n_prec,
  B,
  measure = "ICA",
  mutinfo_estimator = NULL,
  composite,
  seed,
  restr_time = +Inf,
  ncores = 1
)

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.
`B`	Number of bootstrap replications
`measure`	Compute intervals for which measure of surrogacy? Defaults to `"ICA"`. See first column names of `sens_results` for other possibilities.
`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.
`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).
`ncores`	Number of cores used in the sensitivity analysis. The computations are computationally heavy, and this option can speed things up considerably.

Details

Let \hat{\boldsymbol{\beta}} be the estimated identifiable parameter vector, \hat{\Sigma} the corresponding estimated covariance matrix, and \boldsymbol{\nu} a fixed value for the sensitivity parameter. The bootstrap is then performed in the following steps

Resample the identifiable parameters from the estimated sampling distribution,

\hat{\boldsymbol{\beta}}^{(b)} \sim N(\hat{\boldsymbol{\beta}}, \hat{\Sigma}).
For each resampled parameter vector and the fixed sensitivty parameter, compute the ICA as ICA(\hat{\boldsymbol{\beta}}^{(b)}, \boldsymbol{\nu}).