A bidimensional finite mixture model for longitudinal data subject to dropout.
Stat Med
; 37(20): 2998-3011, 2018 09 10.
Article
em En
| MEDLINE
| ID: mdl-29873102
ABSTRACT
In longitudinal studies, subjects may be lost to follow up and, thus, present incomplete response sequences. When the mechanism underlying the dropout is nonignorable, we need to account for dependence between the longitudinal and the dropout process. We propose to model such a dependence through discrete latent effects, which are outcome-specific and account for heterogeneity in the univariate profiles. Dependence between profiles is introduced by using a probability matrix to describe the corresponding joint distribution. In this way, we separately model dependence within each outcome and dependence between outcomes. The major feature of this proposal, when compared with standard finite mixture models, is that it allows the nonignorable dropout model to properly nest its ignorable counterpart. We also discuss the use of an index of (local) sensitivity to nonignorability to investigate the effects that assumptions about the dropout process may have on model parameter estimates. The proposal is illustrated via the analysis of data from a longitudinal study on the dynamics of cognitive functioning in the elderly.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Estudos Longitudinais
/
Modelos Estatísticos
/
Transtornos Cognitivos
/
Perda de Seguimento
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Aged80
/
Female
/
Humans
/
Male
País/Região como assunto:
Europa
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Itália