variance.cmprsk: Compute the variance of the estimates.

In depCensoring: Statistical Methods for Survival Data with Dependent Censoring

Compute the variance of the estimates.

Description

This function computes the variance of the estimates computed by the 'estimate.cmprsk.R' function.

Usage

variance.cmprsk(
  parhatc,
  gammaest,
  data,
  admin,
  conf,
  inst,
  cf,
  eoi.indicator.names,
  Zbin,
  use.chol,
  n.trans,
  totparl
)

Arguments

parhatc	Vector of estimated parameters, computed in the first part of estimate.cmprsk.R.
gammaest	Vector of estimated parameters in the regression model for the control function.
data	A data frame.
admin	Boolean value indicating whether the data contains administrative censoring.
conf	Boolean value indicating whether the data contains confounding and hence indicating the presence of z and, possibly, w.
inst	Variable encoding which approach should be used for dealing with the confounding. inst = "cf" indicates that the control function approach should be used. inst = "W" indicates that the instrumental variable should be used 'as is'. inst = "None" indicates that Z will be treated as an exogenous covariate. Finally, when inst = "oracle", this function will access the argument realV and use it as the values for the control function. Default is inst = "cf".
cf	The control function used to estimate the second step.
eoi.indicator.names	Vector of names of the censoring indicator columns pertaining to events of interest. Events of interest will be modeled allowing dependence between them, whereas all censoring events (corresponding to indicator columns not listed in eoi.indicator.names) will be treated as independent of every other event. If eoi.indicator.names == NULL, all events will be modeled dependently.
Zbin	Indicator value indicating whether (Zbin = TRUE) or not Zbin = FALSE the endogenous covariate is binary. Default is Zbin = NULL, corresponding to the case when conf == FALSE.
use.chol	Boolean value indicating whether the cholesky decomposition was used in estimating the covariance matrix.
n.trans	Number of competing risks in the model (and hence, number of transformation models).
totparl	Total number of covariate effects (including intercepts) in all of the transformation models combined.

Value

Variance estimates of the provided vector of estimated parameters.

depCensoring documentation built on April 4, 2025, 1:52 a.m.