Bayesian Semiparametric Nonlinear Mixed-Effects Joint Models for Data with Skewness, Missing Responses, and Measurement Errors in Covariates
Version of Record online: 7 DEC 2011
© 2011, The International Biometric Society
Volume 68, Issue 3, pages 943–953, September 2012
How to Cite
Huang, Y. and Dagne, G. (2012), Bayesian Semiparametric Nonlinear Mixed-Effects Joint Models for Data with Skewness, Missing Responses, and Measurement Errors in Covariates. Biometrics, 68: 943–953. doi: 10.1111/j.1541-0420.2011.01719.x
- Issue online: 26 SEP 2012
- Version of Record online: 7 DEC 2011
- Received March 2011. Revised October 2011. Accepted October 2011.
- Bayesian analysis;
- Covariate measurement errors;
- Longitudinal data;
- Missing data;
- Random-effects models;
- Skew distributions
Summary It is a common practice to analyze complex longitudinal data using semiparametric nonlinear mixed-effects (SNLME) models with a normal distribution. Normality assumption of model errors may unrealistically obscure important features of subject variations. To partially explain between- and within-subject variations, covariates are usually introduced in such models, but some covariates may often be measured with substantial errors. Moreover, the responses may be missing and the missingness may be nonignorable. Inferential procedures can be complicated dramatically when data with skewness, missing values, and measurement error are observed. In the literature, there has been considerable interest in accommodating either skewness, incompleteness or covariate measurement error in such models, but there has been relatively little study concerning all three features simultaneously. In this article, our objective is to address the simultaneous impact of skewness, missingness, and covariate measurement error by jointly modeling the response and covariate processes based on a flexible Bayesian SNLME model. The method is illustrated using a real AIDS data set to compare potential models with various scenarios and different distribution specifications.