PseudoL: Likelihood function under dependent censoring

In depCensoring: Statistical Methods for Survival Data with Dependent Censoring

Likelihood function under dependent censoring

Description

The PseudoL function is maximized in order to estimate the finite dimensional model parameters, including the dependency parameter. This function assumes that the cumulative hazard function is known.

Usage

PseudoL(theta, resData, X, W, lhat, cumL, cop, dist)

Arguments

theta	Estimated parameter values/initial values for finite dimensional parameters
resData	Data matrix with three columns; Z = the observed survival time, d1 = the censoring indicator of T and d2 = the censoring indicator of C.
X	Data matrix with covariates related to T
W	Data matrix with covariates related to C. First column of W should be ones
lhat	The estimated hazard function obtained from the output of SolveL.
cumL	The estimated cumulative hazard function from the output of SolveL.
cop	Which copula should be computed to account for dependency between T and C. This argument can take one of the values from c("Gumbel", "Frank", "Normal"). The default copula model is "Frank".
dist	The distribution to be used for the dependent censoring C. Only two distributions are allowed, i.e, Weibull and lognormal distributions. With the value "Weibull" as the default.

Value

maximized log-likelihood value

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