SurvRegCensCov (version 1.5)

WeibullIntegrate: Function to be integrated in function SurvRegCens

Description

Function to be integrated to compute log-likelihood function for the Weibull survival regression model with a censored covariate.

Usage

WeibullIntegrate(x, x_i_noncens = NULL, density, param_y_i,
                 param_delta_i, param_lambda, param_gamma,
                 param_beta, intlimit = 10^-10, ForIntegrate = TRUE)

Arguments

x

Value of the censored covariate for observation $i$.

x_i_noncens

Vector of values of the non-censored covariates for observation $i$, i.e. one row of the matrix where each column represents a non-censored covariate.

density

Density function of the censored covariate.

param_y_i

Value of the time-to-event for observation $i$.

param_delta_i

Censoring indicator of time-to-event for observation $i$ (0: censored, 1: not censored).

param_lambda

Scale parameter of the Weibull distribution.

param_gamma

Shape parameter of the Weibull distribution.

param_beta

Regression parameters (i.e. $\beta$): (betaNonCens1, ..., betaNonCens, betaCens)

intlimit

In computation of integrals, values of the function to be integrated below intlimit are set to 0. This makes integration results more accurate and speeds up integration. If the data is such that the absolute values of the underlying baseline Weibull density are very small, i.e. in the range of intlimit, it is advisable to rescale the time variable, e.g. change the scaling from days to years. A very small value of the estimated $\lambda$ is indicative of that situation.

ForIntegrate

logical indicating whether the function is to be integrated or not.

Author

Stanislas Hubeaux,