Longitudinal latent variable models given incompletely observed biomarkers and covariates

• Published in: Statistics in medicine Vol. 35; no. 26; pp. 4729 - 4745
• Main Authors: Ren, Chunfeng, Shin, Yongyun
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 20.11.2016; Wiley Subscription Services, Inc
• Subjects: Algorithms; Biomarkers; Estimation bias; Humans; latent variable; longitudinal data analysis; Longitudinal Studies; Maximum likelihood method; missing data; Models, Statistical; multivariate outcomes; Probability; random effects; Statistics; the EM algorithm; Variables; random effects; the EM algorithm; longitudinal data analysis; latent variable; missing data; multivariate outcomes
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.7022

In this paper, we analyze a two‐level latent variable model for longitudinal data from the National Growth and Health Study where surrogate outcomes or biomarkers and covariates are subject to missingness at any of the levels. A conventional method for efficient handling of missing data is to re‐express the desired model as a joint distribution of variables, including the biomarkers, that are subject to missingness conditional on all of the covariates that are completely observed, and estimate the joint model by maximum likelihood, which is then transformed to the desired model. The joint model, however, identifies more parameters than desired, in general. We show that the over‐identified joint model produces biased estimation of the latent variable model and describe how to impose constraints on the joint model so that it has a one‐to‐one correspondence with the desired model for unbiased estimation. The constrained joint model handles missing data efficiently under the assumption of ignorable missing data and is estimated by a modified application of the expectation‐maximization algorithm. Copyright © 2016 John Wiley & Sons, Ltd.