Modeling Disease Progression with Longitudinal Markers.
J Am Stat Assoc
; 103(481): 259-270, 2008.
Article
en En
| MEDLINE
| ID: mdl-24453387
ABSTRACT
In this paper we propose a Bayesian natural history model for disease progression based on the joint modeling of longitudinal biomarker levels, age at clinical detection of disease and disease status at diagnosis. We establish a link between the longitudinal responses and the natural history of the disease by using an underlying latent disease process which describes the onset of the disease and models the transition to an advanced stage of the disease as dependent on the biomarker levels. We apply our model to the data from the Baltimore Longitudinal Study of Aging on prostate specific antigen (PSA) to investigate the natural history of prostate cancer.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Tipo de estudio:
Observational_studies
/
Prognostic_studies
Idioma:
En
Revista:
J Am Stat Assoc
Año:
2008
Tipo del documento:
Article