A note on posterior predictive checks to assess model fit for incomplete data.
Stat Med
; 35(27): 5029-5039, 2016 11 30.
Article
em En
| MEDLINE
| ID: mdl-27426216
ABSTRACT
We examine two posterior predictive distribution based approaches to assess model fit for incomplete longitudinal data. The first approach assesses fit based on replicated complete data as advocated in Gelman et al. (2005). The second approach assesses fit based on replicated observed data. Differences between the two approaches are discussed and an analytic example is presented for illustration and understanding. Both checks are applied to data from a longitudinal clinical trial. The proposed checks can easily be implemented in standard software like (Win)BUGS/JAGS/Stan. Copyright © 2016 John Wiley & Sons, Ltd.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Estudos Longitudinais
/
Confiabilidade dos Dados
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
Estados Unidos