Paper Info
Title
Modeling Longitudinal Obesity Data with Intermittent Missingness Using a New Latent Variable Model
Abstract
We propose a latent variable model for informative missingness in longitudinal studies which is an extension of latent dropout class model. In our model, the value of the latent variable is affected by the missingness pattern and it is also used as a covariate in modeling the longitudinal response. So the latent variable links the longitudinal response and the missingness process. In our model, the latent variable is continuous instead of categorical and we assume that it is from a normal distribution. The EM algorithm is used to obtain the estimates of the parameter we are interested in and Gauss-Hermite quadrature is used to approximate the integration of the latent variable. The standard errors of the parameter estimates can be obtained from the bootstrap method or from the inverse of the Fisher information matrix of the final marginal likelihood. Comparisons are made to the mixed model and complete-case analysis in terms of a clinical trial dataset, which is Weight Gain Prevention among Women (WGPW) study. We use the generalized Pearson residuals to assess the fit of the proposed latent variable model.
Year
DOI
Venue
2016
10.1080/03610918.2014.889154
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Keywords
Field
DocType
EM algorithm,Intermittent missingness,Latent variable,Longitudinal data,62J12,62L20,62P15
Econometrics,Covariate,Expectation–maximization algorithm,Categorical variable,Latent variable model,Latent class model,Marginal likelihood,Latent variable,Missing data,Statistics,Mathematics
Journal
Volume
Issue
ISSN
45
6
0361-0918
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
li qin100.34
lisa a weissfeld283.10
Marsha D. Marcus300.34
Michele D. Levine400.34
feng dai540.76