Adaptively leveraging external data with robust meta-analytical-predictive prior using empirical Bayes.
Pharm Stat
; 22(5): 846-860, 2023.
Article
em En
| MEDLINE
| ID: mdl-37220997
ABSTRACT
The robust meta-analytical-predictive (rMAP) prior is a popular method to robustly leverage external data. However, a mixture coefficient would need to be pre-specified based on the anticipated level of prior-data conflict. This can be very challenging at the study design stage. We propose a novel empirical Bayes robust MAP (EB-rMAP) prior to address this practical need and adaptively leverage external/historical data. Built on Box's prior predictive p-value, the EB-rMAP prior framework balances between model parsimony and flexibility through a tuning parameter. The proposed framework can be applied to binomial, normal, and time-to-event endpoints. Implementation of the EB-rMAP prior is also computationally efficient. Simulation results demonstrate that the EB-rMAP prior is robust in the presence of prior-data conflict while preserving statistical power. The proposed EB-rMAP prior is then applied to a clinical dataset that comprises 10 oncology clinical trials, including the prospective study.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Projetos de Pesquisa
/
Modelos Estatísticos
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Pharm Stat
Assunto da revista:
FARMACOLOGIA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos