A Bayesian semiparametric joint hierarchical model for longitudinal and survival data.
Biometrics
; 59(2): 221-8, 2003 Jun.
Article
en En
| MEDLINE
| ID: mdl-12926706
ABSTRACT
This article proposes a new semiparametric Bayesian hierarchical model for the joint modeling of longitudinal and survival data. We relax the distributional assumptions for the longitudinal model using Dirichlet process priors on the parameters defining the longitudinal model. The resulting posterior distribution of the longitudinal parameters is free of parametric constraints, resulting in more robust estimates. This type of approach is becoming increasingly essential in many applications, such as HIV and cancer vaccine trials, where patients' responses are highly diverse and may not be easily modeled with known distributions. An example will be presented from a clinical trial of a cancer vaccine where the survival outcome is time to recurrence of a tumor. Immunologic measures believed to be predictive of tumor recurrence were taken repeatedly during follow-up. We will present an analysis of this data using our new semiparametric Bayesian hierarchical joint modeling methodology to determine the association of these longitudinal immunologic measures with time to tumor recurrence.
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Colección:
01-internacional
Asunto principal:
Análisis de Supervivencia
/
Modelos Estadísticos
/
Teorema de Bayes
Tipo de estudio:
Clinical_trials
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Biometrics
Año:
2003
Tipo del documento:
Article
País de afiliación:
Estados Unidos