A Bayesian Approach to Joint Mixed-Effects Models with a Skew-Normal Distribution and Measurement Errors in Covariates
Article first published online: 10 MAY 2010
© 2010, The International Biometric Society
Volume 67, Issue 1, pages 260–269, March 2011
How to Cite
Huang, Y. and Dagne, G. (2011), A Bayesian Approach to Joint Mixed-Effects Models with a Skew-Normal Distribution and Measurement Errors in Covariates. Biometrics, 67: 260–269. doi: 10.1111/j.1541-0420.2010.01425.x
- Issue published online: 14 MAR 2011
- Article first published online: 10 MAY 2010
- Received September 2009. Revised February 2010. Accepted March 2010.
- Bayesian approach;
- Longitudinal data;
- Measurement errors;
- Nonlinear mixed-effects models;
- Skew-normal distribution
Summary In recent years, nonlinear mixed-effects (NLME) models have been proposed for modeling complex longitudinal data. Covariates are usually introduced in the models to partially explain intersubject variations. However, one often assumes that both model random error and random effects are normally distributed, which may not always give reliable results if the data exhibit skewness. Moreover, some covariates such as CD4 cell count may be often measured with substantial errors. In this article, we address these issues simultaneously by jointly modeling the response and covariate processes using a Bayesian approach to NLME models with covariate measurement errors and a skew-normal distribution. A real data example is offered to illustrate the methodologies by comparing various potential models with different distribution specifications. It is showed that the models with skew-normality assumption may provide more reasonable results if the data exhibit skewness and the results may be important for HIV/AIDS studies in providing quantitative guidance to better understand the virologic responses to antiretroviral treatment.