Cause-specific hazard regression for competing risks data under interval censoring and left truncation

Bivariate Competing Risks Models Under Random Left Truncation and Right Censoring

In survival or reliability studies, it is common to have truncated data due to the limited time span of the study or dropouts of the subjects for various reasons. The estimation of survivor function under left truncation was first discussed by Kaplan and Meier by extending the well known productlimit estimator of the survivor function. The focus of this paper is on the nonparametric estimation ...

Marginal Regression Analysis for Semi-Competing Risks Data Under Dependent Censoring

Multiple events data are commonly seen in medical applications. There are two types of events, namely terminal and non-terminal. Statistical analysis for non-terminal events is complicated due to dependent censoring. Consequently, joint modelling and inference are often needed to avoid the problem of non-identifiability. This article considers regression analysis for multiple events data with m...

Analysis of Partly Interval-Censored Data under Competing Risks Framework

Competing risks regression for clustered data.

A population average regression model is proposed to assess the marginal effects of covariates on the cumulative incidence function when there is dependence across individuals within a cluster in the competing risks setting. This method extends the Fine-Gray proportional hazards model for the subdistribution to situations, where individuals within a cluster may be correlated due to unobserved s...

Analysis of Progressive Censoring Competing Risks Data with Binomial Removals

In several studies in reliability and in medical science, the cause of failure/death of items or individuals may be attributable to more then one cause. In this paper, we will study the competing risks model when the data is progressively Type-II censored with random removals. We study the model under the assumption of independent causes of failure and exponential lifetimes, where the number of...