Survival analysis, the analysis of failure time data, is a rapid developing area and a number
of books on the topic have been published in last twenty-five years. However, all of these …

It is shown that variance estimates for regression coefficients in exposure‐stratified case‐
cohort studies (Borgan et al., Lifetime Data Anal., 6, 2000, 39–58) can easily be obtained …

This paper deals with hazard regression models for survival data with time‐dependent
covariates consisting of updated quantitative measurements. The main emphasis is on the …

Marginal additive hazards models are considered for multivariate survival data in which
individuals may experience events of several types and there may also be correlation …

We propose an additive hazards model with latent variables to investigate the observed and
latent risk factors of the failure time of interest. Each latent risk factor is characterized by …

This paper discusses regression analysis of current status failure time data arising from the
additive hazards model in the presence of informative censoring. Many methods have been …

Cure rate models explicitly account for the survival fraction in failure time data. When the
covariates are measured with errors, naively treating mismeasured covariates as error-free …

Covariate measurement error has attracted extensive interest in survival analysis. Since
Prentice, a large number of inference methods have been developed to handle error-prone …

We propose a novel additive mean residual life model to examine the effects of observable
and latent risk factors on the mean residual life function of interest in the presence of right …

It is well established that measurement error has drastically negative impact on data
analysis. It can not only bias parameter estimates but may also cause loss of power for …