Modeling multivariate binary responses with multiple levels of nesting based on alternating logistic regressions: an application to caries aggregation.
J Dent Res
; 83(10): 776-81, 2004 Oct.
Article
em En
| MEDLINE
| ID: mdl-15381718
ABSTRACT
Clustered binary responses are commonly encountered in dental research. Data analysis may include modeling both the marginal response probabilities (i.e., risk) and the dependence structure between pairs of responses (i.e., aggregation). While second-order generalized estimating equations (GEE2) is a well-known approach for such data, alternating logistic regressions (ALR) is a computationally efficient alternative method, especially for large clusters. We illustrate ALR with an application to caries aggregation using a dataset with 3 levels of nesting tooth surfaces within an interproximal (IP) region, IP regions within a jaw, and jaws within a subject. Caries lesions appear to aggregate strongly within subjects with a spatially distributed risk. The minimum within-IP-region odds ratio (OR) was 2.25 (95% confidence interval 1.15, 4.41), and the within-IP-region ORs were always greater than the between-IP-region ORs. ALR is a convenient and useful regression technique for explicit modeling of the dependence structure, and may be applicable to other dental research problems involving clustered or nested responses.
Buscar no Google
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Cárie Dentária
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
J Dent Res
Ano de publicação:
2004
Tipo de documento:
Article
País de afiliação:
Estados Unidos