Joint modeling of survival and longitudinal data: likelihood approach revisited.
Biometrics
; 62(4): 1037-43, 2006 Dec.
Article
em En
| MEDLINE
| ID: mdl-17156277
ABSTRACT
The maximum likelihood approach to jointly model the survival time and its longitudinal covariates has been successful to model both processes in longitudinal studies. Random effects in the longitudinal process are often used to model the survival times through a proportional hazards model, and this invokes an EM algorithm to search for the maximum likelihood estimates (MLEs). Several intriguing issues are examined here, including the robustness of the MLEs against departure from the normal random effects assumption, and difficulties with the profile likelihood approach to provide reliable estimates for the standard error of the MLEs. We provide insights into the robustness property and suggest to overcome the difficulty of reliable estimates for the standard errors by using bootstrap procedures. Numerical studies and data analysis illustrate our points.
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Base de dados:
MEDLINE
Assunto principal:
Funções Verossimilhança
/
Análise de Sobrevida
/
Estudos Longitudinais
/
Modelos Estatísticos
Tipo de estudo:
Observational_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2006
Tipo de documento:
Article
País de afiliação:
Estados Unidos