twostep_SurvSurv: Fit survival-survival copula submodel with two-step estimator

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Fit survival-survival copula submodel with two-step estimator

Description

The twostep_SurvSurv() function fits the copula (sub)model for a time-to-event surrogate and true endpoint with a two-step estimator. In the first step, the marginal distribution parameters are estimated through maximum likelihood. In the second step, the copula parameter is estimate while holding the marginal distribution parameters fixed.

Usage

twostep_SurvSurv(
  X,
  delta_X,
  Y,
  delta_Y,
  copula_family,
  n_knots,
  method = "BFGS"
)

Arguments

X	(numeric) Possibly right-censored time-to-surrogate event
delta_X	(integer) Surrogate event indicator: 1L if surrogate event ocurred. 0L if censored.
Y	(numeric) Possibly right-censored time-to-true endpoint event
delta_Y	(integer) True endpoint event indicator: 1L if true endpoint event ocurred. 0L if censored.
copula_family	Copula family, one of the following: "clayton" "frank" "gumbel" "gaussian" The parameterization of the respective copula families can be found in the help files of the dedicated functions named copula_loglik_copula_scale().
n_knots	Number of internal knots for the Royston-Parmar survival model.
method	Optimization algorithm for maximizing the objective function. For all options, see ?maxLik::maxLik. Defaults to "BFGRS".

Value

A list with three elements:

• ml_fit: object of class maxLik::maxLik that contains the estimated copula model.

• marginal_S_dist: object of class fitdistrplus::fitdist that represents the marginal surrogate distribution.

• copula_family: string that indicates the copula family

Surrogate documentation built on Sept. 25, 2023, 5:07 p.m.