Parametric survival models for interval-censored data with time-dependent covariates

Parametric survival models for interval-censored data with time-dependent covariates.

We present a parametric family of regression models for interval-censored event-time (survival) data that accomodates both fixed (e.g. baseline) and time-dependent covariates. The model employs a three-parameter family of survival distributions that includes the Weibull, negative binomial, and log-logistic distributions as special cases, and can be applied to data with left, right, interval, or...

Estimation with correlated censored survival data with missing covariates.

Incomplete covariate data are a common occurrence in studies in which the outcome is survival time. Further, studies in the health sciences often give rise to correlated, possibly censored, survival data. With no missing covariate data, if the marginal distributions of the correlated survival times follow a given parametric model, then the estimates using the maximum likelihood estimating equat...

Space-time models with time-dependent covariates

Survival Data Analysis with Time-Dependent Covariates Using Generalized Additive Models

We discuss a flexible method for modeling survival data using penalized smoothing splines when the values of covariates change for the duration of the study. The Cox proportional hazards model has been widely used for the analysis of treatment and prognostic effects with censored survival data. However, a number of theoretical problems with respect to the baseline survival function remain unsol...

Nonparametric regression with censored survival time data

The paper deals with a class of nonparametric regression estimates introduced by Beran (1981) to estimate conditional survival functions in the presence of right censoring. Weak convergence results are established for kernel and nearest neighbour estimates of the conditional cumulative hazard and survival functions as well as the quantile and L-type regression functionals.