A Joint Frailty Model for Competing Risks Survival Data

A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects.

This article studies a general joint model for longitudinal measurements and competing risks survival data. The model consists of a linear mixed effects sub-model for the longitudinal outcome, a proportional cause-specific hazards frailty sub-model for the competing risks survival data, and a regression sub-model for the variance-covariance matrix of the multivariate latent random effects based...

Parametric Estimation in a Recurrent Competing Risks Model

A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the compet- ing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails. ...

Simulating competing risks data in survival analysis.

Competing risks analysis considers time-to-first-event ('survival time') and the event type ('cause'), possibly subject to right-censoring. The cause-, i.e. event-specific hazards, completely determine the competing risk process, but simulation studies often fall back on the much criticized latent failure time model. Cause-specific hazard-driven simulation appears to be the exception; if done, ...

Additive Hazard Model with Additive Frailty for Semi-Competing Risks Data

We proposed an illness-death model with Lin and Ying's additive hazard and additive frailty for the regression analysis on semi-competing risks problem in a general morbidity/mortality process. Comparing with the Cox-type hazard, the additive hazard function is more natural and properly partitions the effect of the covariate on one transition into the other transition, internal consistency in t...

Classification Trees for Survival Data with Competing Risks