Modelling geographically referenced survival data with a cure fraction.
Stat Methods Med Res
; 15(4): 307-24, 2006 Aug.
Article
em En
| MEDLINE
| ID: mdl-16886733
ABSTRACT
The emergence of geographical information systems and related softwares nowadays enables medical databases to incorporate the geographical information on patients, allowing studies in spatial associations. Public health administrators and researchers are often interested in detecting variation in survival patterns by region or county in order to understand the possible factors that contribute towards such spatial discrepancies. These issues have led statisticians to develop survival models that account for spatial clustering and variation. Additionally, with rapid developments in medical and health sciences, researchers increasingly encounter data sets where a substantial portion of patients are cured. Models accounting for cure in the population assist in the prognosis of potentially terminal diseases. This article proposes a Bayesian modelling framework that models spatial associations for areally referenced survival data using a general class of cure models proposed by Cooner et al. The special models we outline are alternatives to the traditional proportional hazards models and can be fitted using standard Bayesian software such as WinBUGS.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
1_ASSA2030
Problema de saúde:
1_sistemas_informacao_saude
Assunto principal:
Análise de Sobrevida
/
Modelos Estatísticos
/
Análise de Pequenas Áreas
/
Sistemas de Informação Geográfica
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Stat Methods Med Res
Ano de publicação:
2006
Tipo de documento:
Article
País de afiliação:
Estados Unidos