Bayesian Semiparametric Mixture Tobit Models with Left Censoring, Skewness, and Covariate Measurement Errors
Document Type
Article
Publication Date
2013
Keywords
measurement error, mixed-effects models, mixture Tobit models, skew distributions
Digital Object Identifier (DOI)
https://doi.org/10.1002/sim.5799
Abstract
Common problems to many longitudinal HIV/AIDS, cancer, vaccine, and environmental exposure studies are the presence of a lower limit of quantification of an outcome with skewness and time-varying covariates with measurement errors. There has been relatively little work published simultaneously dealing with these features of longitudinal data. In particular, left-censored data falling below a limit of detection may sometimes have a proportion larger than expected under a usually assumed log-normal distribution. In such cases, alternative models, which can account for a high proportion of censored data, should be considered. In this article, we present an extension of the Tobit model that incorporates a mixture of true undetectable observations and those values from a skew-normal distribution for an outcome with possible left censoring and skewness, and covariates with substantial measurement error. To quantify the covariate process, we offer a flexible nonparametric mixed-effects model within the Tobit framework. A Bayesian modeling approach is used to assess the simultaneous impact of left censoring, skewness, and measurement error in covariates on inference. The proposed methods are illustrated using real data from an AIDS clinical study. Copyright © 2013 John Wiley & Sons, Ltd.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Statistics in Medicine, v. 32, issue 22, p. 3881-3898
Scholar Commons Citation
Dagne, Getachew A. and Huang, Yangxin, "Bayesian Semiparametric Mixture Tobit Models with Left Censoring, Skewness, and Covariate Measurement Errors" (2013). Epidemiology and Biostatistics Faculty Publications. 47.
https://digitalcommons.usf.edu/epb_facpub/47
