LoglikWeibullSurvRegCens {SurvRegCensCov}

Log-likelihood function of a Weibull Survival Regression Model allowing for an interval-censored covariate.

Description

Computes the log-likelihood function of a Weibull Survival Regression Model allowing for an interval-censored covariate.

Usage

LoglikWeibullSurvRegCens(x, data_y, data_delta_loglik, data_cov_noncens = NULL, 
                         data_cov_cens, density, data_r_loglik, data_lowerbound,
                         intlimit = 10^-10)

Arguments

x

Vector of parameters, ordered as follows: Scale parameter, Shape parameter, regression parameters (i.e. \beta) linked to the non-censored covariates, regression parameter (i.e. \beta) linked to the censored covariate.

data_y

Time-to-event vector.

data_delta_loglik

Censored indicator vector of the time-to-event (0: censored, 1: not censored).

data_cov_noncens

Matrix where each column represents a non-censored covariate.

data_cov_cens

Censored covariate vector.

density

Density function of the censored covariate.

data_r_loglik

Censored indicator vector of the censored covariate (0: censored, 1: not censored).

data_lowerbound

A vector which corresponds to the lower bounds for the interval-censored observations of the censored covariate. If no lower bound is available then put NA.

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.

Note

Function not intended to be invoked by the user.

Author(s)

Stanislas Hubeaux, [e-mail]

Kaspar Rufibach, [e-mail]
http://www.kasparrufibach.ch

References

Hubeaux, S. (2013). Parametric Surival Regression Model with left- and/or interval-censored covariate. Technical report, Biostatistics Oncology, F. Hoffmann-La Roche Ltd.

Sattar, A., Sinha, S. K. and Morris, N. J. (2012). A Parametric Survival Model When a Covariate is Subject to Left-Censoring. Biometrics & Biostatistics, S3(2).