Exponential Stayability Model with Censoring and Covariates

A Joint Model for Nonlinear Mixed-Effects Models with Censoring and Covariates Measured with Error

In recent years AIDS researchers have shown great interest in the study of HIV viral dynamics. Nonlinear mixed-effect models (NLME) have been proposed for modeling the intraand inter-patients variations. The inter-patients variation often receives great attention and may be partially explained by time-varying covariates such as CD4 cell counts. Statistical analyses in these studies are complica...

Adjusting for Dependent Censoring Using Many Covariates

Right-censored data are common in many epidemiological studies. One main goal is to estimate the survival function of lifetime. However, if this right-censoring is dependent and is explained by high-dimensional covariates, estimating the survival function of lifetime by using either semiparametric models or nonparametric methods can be problematic. In this paper, we condense these high-dimensio...

Locally optimal tests for exponential distributions with type-I censoring

This article studies a locally optimal test φ for testing H0 : θ ≥ θ0 versus H1 : θ < θ0 for the lifetime parameter θ in an exponential distribution based on type-I censored data. Certain properties associated with φ are addressed. We compare the performance of φ with that of Spurrier and Wei’s [Spurrier, J.D., Wei, L.J., 1980. A test of the parameter of the exponential distribution in the type...

Informative Censoring in Piecewise Exponential Survival Models

There are often reasons to suppose that there is dependence between the time to event and time to censoring, or informative censoring, for survival data, particularly when considering medical data. This is because the decision to treat or not is often made according to prognosis, usually with the most ill patients being prioritised. Due to identifiability issues, sensitivity analyses are often ...

Supplementary Materials for: “Analysis of Proportional Odds Models with Censoring and Errors-in-Covariates”