A multiple imputation approach to nonlinear mixed-effects models with covariate measurement errors and missing values

Abstract

In longitudinal studies, nonlinear mixed-effects models have been widely applied to describe the intra- and the inter-subject variations in data. The inter-subject variation usually receives great attention and it may be partially explained by time-dependent covariates. However, some covariates may be measured with substantial errors and may contain missing values. We proposed a multiple imputation method, implemented by a Markov Chain Monte-Carlo method along with Gibbs sampler, to address the covariate measurement errors and missing data in nonlinear mixed-effects models. The multiple imputation method is illustrated in a real data example. Simulation studies show that the multiple imputation method outperforms the commonly used naive methods.

Keywords:

• longitudinal data
• measurement error
• MCMC method
• missing data
• multiple imputation

Acknowledgements

The research was supported by Canada Natural Sciences and Engineering Research Council (NSERC) discovery grant to Wei Liu, and by National Natural Science Foundation of China (Grant No. 11271064) to Shuyou Li.