Multivariate t nonlinear mixed-effects models for multi-outcome longitudinal data with missing values.
Stat Med
; 33(17): 3029-46, 2014 Jul 30.
Article
em En
| MEDLINE
| ID: mdl-24634345
ABSTRACT
The multivariate nonlinear mixed-effects model (MNLMM) has emerged as an effective tool for modeling multi-outcome longitudinal data following nonlinear growth patterns. In the framework of MNLMM, the random effects and within-subject errors are assumed to be normally distributed for mathematical tractability and computational simplicity. However, a serious departure from normality may cause lack of robustness and subsequently make invalid inference. This paper presents a robust extension of the MNLMM by considering a joint multivariate t distribution for the random effects and within-subject errors, called the multivariate t nonlinear mixed-effects model. Moreover, a damped exponential correlation structure is employed to capture the extra serial correlation among irregularly observed multiple repeated measures. An efficient expectation conditional maximization algorithm coupled with the first-order Taylor approximation is developed for maximizing the complete pseudo-data likelihood function. The techniques for the estimation of random effects, imputation of missing responses and identification of potential outliers are also investigated. The methodology is motivated by a real data example on 161 pregnant women coming from a study in a private fertilization obstetrics clinic in Santiago, Chile and used to analyze these data.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Funções Verossimilhança
/
Análise Multivariada
/
Estudos Longitudinais
/
Dinâmica não Linear
Tipo de estudo:
Etiology_studies
/
Observational_studies
/
Prognostic_studies
Limite:
Adult
/
Female
/
Humans
/
Pregnancy
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article