Department of Statistical Science
Duke University
presents:
Sujit K. Ghosh
North Carolina State University
"Latent Waiting Time Models For Bivariate Event Times With Censoring"
Abstract: Multivariate event time data arises frequently in both medical and industrial settings. In such data sets: event times are often associated with quite different occurrences, event times can not be considered as independent - the distribution of time to occurrence of one event may change after the occurrence of another, events can occur simultaneously, available covariate information may provide useful explanation. Censoring in some of the observations, both partial and complete, occurs. Focusing on the bivariate case we formulate models rich enough to accommodate these points. Such models are built using latent waiting times. These latent variables are assumed to follow general proportional hazards or accelerated life models. We develop a straightforward fitting procedure using data augmentation which routinely handles censoring. We conclude with the analysis of a sample of bivariate event times for patients with a clinical diagnosis of AIDS.
October 4, 1996
4:30 pm - 5:30 pm
116 Old Chem Building Any questions concerning the seminar may be addressed to Cheryl McGhee @[919] 684-8029, e-mail cheryl@stat.duke.edu