Loading...
Publication
Bayesian joint modeling of longitudinal and spatial survival AIDS data
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 13.11 MB | Adobe PDF |
Advisor(s)
Abstract(s)
"Joint analysis of longitudinal and survival data has received increasing attention
in the recent years, especially for analyzing cancer and AIDS data. As both
repeated measurements (longitudinal) and time-to-event (survival) outcomes are
observed in an individual, a joint modeling is more appropriate because it takes
into account the dependence between the two types of responses, which are often
analyzed separately. We propose a Bayesian hierarchical model for jointly modeling
longitudinal and survival data considering functional time and spatial frailty effects,
respectively. That is, the proposed model deals with nonlinear longitudinal effects
and spatial survival effects accounting for the unobserved heterogeneity among individuals
living in the same region. This joint approach is applied to a cohort study of
patients with HIV/AIDS in Brazil during the years 2002–2006. Our Bayesian joint
model presents considerable improvements in the estimation of survival times of the
Brazilian HIV/AIDS patients when compared with those ones obtained through a
separate survival model and shows that the spatial risk of death is the same across
the different Brazilian states."
Description
Keywords
Joint model Repeated measurements Bayesian analysis Time-to-event data Spatial frailty
Citation
Martins, R., Silva, G. L., and Andreozzi, V. (2016) Bayesian joint modeling of longitudinal and spatial survival AIDS data. Statist. Med., 35: 3368–3384. doi: 10.1002/sim.6937