A Flowgraph Competing Risks Model with an Application on Kidney
Transplants
When: Thursday, Nov. 15, 07, 2:00pm -- 3:00pm
Where: Room 118, Mathematical Building, UNO
Abstract:
Flowgraph models are versatile data analytical tools for multi-state
time-to-event data that form a semi-Markov process. Most of the recent
researches and applications are within Bayesian framework. In this
manuscript, we model two competing risks using flowgraph, and provide
standard errors for maximum likelihood estimates of a covariate. The
method is illustrated using kidney transplant data collected at Tulane
Abdominal Transplant Institute of Tulane Medical Center in New Orleans,
Louisiana.