Bayesian analysis of multivariate mixed models for a prospective cohort study using skew-elliptical distributions.
Biom J
; 55(4): 495-508, 2013 Jul.
Article
en En
| MEDLINE
| ID: mdl-23609779
ABSTRACT
Classical multivariate mixed models that acknowledge the correlation of patients through the incorporation of normal error terms are widely used in cohort studies. Violation of the normality assumption can make the statistical inference vague. In this paper, we propose a Bayesian parametric approach by relaxing this assumption and substituting some flexible distributions in fitting multivariate mixed models. This strategy allows for the skewness and the heavy tails of error-term distributions and thus makes inferences robust to the violation. This approach uses flexible skew-elliptical distributions, including skewed, fat, or thin-tailed distributions, and imposes the normal model as a special case. We use real data obtained from a prospective cohort study on the low back pain to illustrate the usefulness of our proposed approach.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Biometría
Tipo de estudio:
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Biom J
Año:
2013
Tipo del documento:
Article
País de afiliación:
Irán