Markov counting models for correlated binary responses.
Biostatistics
; 16(3): 427-40, 2015 Jul.
Article
em En
| MEDLINE
| ID: mdl-25792624
ABSTRACT
We propose a class of continuous-time Markov counting processes for analyzing correlated binary data and establish a correspondence between these models and sums of exchangeable Bernoulli random variables. Our approach generalizes many previous models for correlated outcomes, admits easily interpretable parameterizations, allows different cluster sizes, and incorporates ascertainment bias in a natural way. We demonstrate several new models for dependent outcomes and provide algorithms for computing maximum likelihood estimates. We show how to incorporate cluster-specific covariates in a regression setting and demonstrate improved fits to well-known datasets from familial disease epidemiology and developmental toxicology.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Cadeias de Markov
/
Modelos Estatísticos
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Child
/
Humans
País como assunto:
America do sul
/
Brasil
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article